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Yong Luo

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38 papers

1 author row

AAAI Conference 2026 Conference Paper

Physics-Informed Multi-Task Learning for Battery State of Health Prediction with Uncertainty Quantification

Tianwen Zhu
Guangyu Wu
Zhiwei Cao
Ruihang Wang
Jimin Jia
Yong Luo
Yonggang Wen

Existing battery State of Health (SOH) prediction approaches often struggle to provide both accurate predictions and reliable uncertainty estimates. This paper presents a novel Multi-Task Learning (MTL) framework that jointly tackles SOH prediction and provides a proxy metric for uncertainty through a unified architecture. The framework combines a Physics-Informed Neural Network (PINN) for SOH prediction with a deep autoencoding Gaussian mixture model for uncertainty modeling. Particularly, the energy score from the Gaussian mixture model serves as a proxy metric for uncertainty, where a higher score indicates potential prediction unreliability. Moreover, to enhance task-specific learning, we employ a multi-head attention mechanism that adaptively captures distinct feature relationships. Our experiments show improvements in prediction performance compared to the state-of-the-art baseline. A comprehensive evaluation on six XJTU battery benchmark datasets demonstrates that our framework achieves a prediction accuracy of 99.50% (MAPE: 0.0050) while providing reliable uncertainty quantification through the proxy metric.

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AAAI Conference 2026 Conference Paper

Rethinking Visual Token Reduction in LVLMs Under Cross-Modal Misalignment

Rui Xu
Yunke Wang
Yong Luo
Bo Du

Large Vision-Language Models (LVLMs) encode visual inputs as dense sequences of patch-level tokens to capture fine-grained semantics. These visual tokens often outnumber their textual counterparts by a large margin, leading to substantial computational overhead and limiting the scalability of LVLMs in practice. Previous efforts have explored visual token reduction either prior to or within the large language models (LLMs). However, most in-LLM reduction approaches rely on text-conditioned interactions, implicitly assuming that textual tokens can reliably capture the importance of visual tokens. In this work, we revisit this assumption and reveal causal, semantic, and spatial forms of cross-modal misalignment. These misalignments undermine the effectiveness of text-guided visual token reduction. To address this, we introduce VisionDrop, a training-free, visual-only pruning framework that selects informative visual tokens based on intra-modal (visual-to-visual) attention, without relying on textual signals. To further suppress redundancy throughout the model hierarchy, we treat the visual encoder and the LLM as a unified system and design a progressive pruning pipeline. Our method performs dominant token selection and lightweight contextual merging at multiple stages, enabling fine-grained visual information to be retained even under aggressive token budgets. Extensive experiments across diverse benchmarks show that VisionDrop achieves consistent improvements over existing approaches, despite requiring no additional training or complex modifications. Notably, when integrated with LLaVA-NeXT-7B, VisionDrop achieves a 2.7x reduction in inference latency and 6x in FLOPs, while retaining 95.71% of the original performance.

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AAAI Conference 2026 Conference Paper

SRD: Reinforcement-Learned Semantic Perturbation for Backdoor Defense in VLMs

Shuhan Xu
Siyuan Liang
Hongling Zheng
Aishan Liu
Xinbiao Wang
Yong Luo
Fu Lin
Leszek Rutkowski

Visual language models (VLMs) have made significant progress in image captioning tasks, yet recent studies have found they are vulnerable to backdoor attacks. Attackers can inject undetectable perturbations into the data during inference, triggering abnormal behavior and generating malicious captions. These attacks are particularly challenging to detect and defend against due to the stealthiness and cross-modal propagation of the trigger signals. In this paper, we identify two key vulnerabilities by analyzing existing attack patterns: (1) the model exhibits abnormal attention concentration on certain regions of the input image, and (2) backdoor attacks often induce semantic drift and sentence incoherence. Based on these insights, we propose Semantic Reward Defense (SRD), a reinforcement learning framework that mitigates backdoor behavior without requiring any prior knowledge of trigger patterns. SRD learns to apply discrete perturbations to sensitive contextual regions of image inputs via a deep Q-network policy, aiming to confuse attention and disrupt the activation of malicious paths. To guide policy optimization, we design a reward signal named semantic fidelity score, which jointly assesses the semantic consistency and linguistic fluency of the generated captions, encouraging the agent to achieve a robust yet faithful output. SRD offers a trigger-agnostic, policy-interpretable defense paradigm that effectively mitigates local (TrojVLM) and global (Shadowcast) backdoor attacks, reducing ASR to 3.6% and 5.6% respectively, with less than 15% average CIDEr drop on the clean inputs.

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JBHI Journal 2026 Journal Article

Tumor Contraction-Aware Multi-Sequence MRI Framework for Accurate Post-Ablation Margin Assessment in Hepatocellular Carcinoma

Linan Dong
Hongwei Ge
Jie Yu
Yong Luo
Jinming Hu
Shichen Yu
Ping Liang

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality, and microwave ablation (MWA) is commonly used for patients ineligible for surgical resection. A critical challenge following MWA is the assessment of the ablative margin, which is complicated by non-diffeomorphic deformations introduced by thermal effects during the procedure. This paper proposes a Multi-sequence Distance-guided Complementary Network (MDCNet) that utilizes multi-sequence MRI to quantify the extent of tumor contraction after MWA. To account for the differential contraction responses of liver parenchyma and tumor tissue, we propose a novel distance-aware mask transformation strategy. This method explicitly models the spatial attenuation of MWA energy and approximates the influence of liver parenchyma's linear elastic response on tumor shrinkage, thereby enhancing the spatial adaptiveness of feature weighting. To capture the distinct structural characteristics of liver tissue emphasized by different MRI sequences and to leverage their complementary information, a gated channel fusion module is introduced to dynamically integrate features from delayed-phase and T2-weighted images. To validate the practical effectiveness of our proposed method, we evaluate the ablative margins of 115 HCC patients using a fine-tuned TransMorph model that incorporated tumor contraction predictions generated by MDCNet, and compare the results with radiologist 2D assessments. The registration method enhanced with MDCNet improved tumor deformation accuracy and achieved a higher Youden Index in detecting incomplete ablations. Moreover, MDCNet provides interpretable predictions, thereby facilitating clinical decision support.

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IJCAI Conference 2025 Conference Paper

A Dual Stream Visual Tokenizer for LLM Image Generation

Yongqian Li
Yong Luo
Xiantao Cai
Zheng He
Zhennan Meng
Nidong Wang
Yunlin Chen
Zhifei Li

We proposes a novel visual tokenizer by combining high-level semantic tokens and low-level pixel tokens to represent images, aiming to address the challenges of image-to-sequence conversion for Large Language Models (LLMs). Existing visual tokenizers, such as VQ-VAE and diffusion-based models, either struggle with token explosion as image resolution increases or fail to capture detailed structural information. Our method introduces a dual-token system: high-level semantic tokens capture the main content of the image, while low-level pixel tokens preserve structural details. By integrating these tokens in a hybrid architecture, we leverage a VQ-VAE branch to generate low-resolution guidance and a diffusion process to reconstruct high-resolution images with both semantic coherence and structural accuracy. This approach significantly reduces the number of required tokens and enhances image reconstruction quality, offering an efficient solution for tasks like image generation and understanding based on LLMs.

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NeurIPS Conference 2025 Conference Paper

AiDE-Q: Synthetic Labeled Datasets Can Enhance Learning Models for Quantum Property Estimation

Xinbiao Wang
Yuxuan Du
Zihan Lou
Yang Qian
Kaining Zhang
Yong Luo
Bo Du
Dacheng Tao

Quantum many-body problems are central to various scientific disciplines, yet their ground-state properties are intrinsically challenging to estimate. Recent advances in deep learning (DL) offer potential solutions in this field, complementing prior purely classical and quantum approaches. However, existing DL-based models typically assume access to a large-scale and noiseless labeled dataset collected by infinite sampling. This idealization raises fundamental concerns about their practical utility, especially given the limited availability of quantum hardware in the near term. To unleash the power of these DL-based models, we propose AiDE-Q (\underline{a}utomat\underline{i}c \underline{d}ata \underline{e}ngine for \underline{q}uantum property estimation), an effective framework that addresses this challenge by iteratively generating high-quality synthetic labeled datasets. Specifically, AiDE-Q utilizes a confidence-check method to assess the quality of synthetic labels and continuously improves the employed DL models with the identified high-quality synthetic dataset. To verify the effectiveness of AiDE-Q, we conduct extensive numerical simulations on a diverse set of quantum many-body and molecular systems, with up to 50 qubits. The results show that AiDE-Q enhances prediction performance for various reference learning models, with improvements of up to $14. 2\\%$. Moreover, we exhibit that a basic supervised learning model integrated with AiDE-Q outperforms advanced reference models, highlighting the importance of a synthetic dataset. Our work paves the way for more efficient and practical applications of DL for quantum property estimation.

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AAAI Conference 2025 Conference Paper

Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

Wenbin Wang
Liang Ding
Minyan Zeng
Xiabin Zhou
Li Shen
Yong Luo
Wei Yu
Dacheng Tao

Multimodal large language models (MLLMs) have experienced significant advancements recently, but still struggle to recognize and interpret intricate details in high-resolution (HR) images effectively. While state-of-the-art (SOTA) MLLMs claim to process images at 4K resolution, existing MLLM benchmarks only support up to 2K, leaving the capabilities of SOTA models on true HR images largely untested. Furthermore, existing methods for enhancing HR image perception in MLLMs rely on computationally expensive visual instruction tuning. To address these limitations, we introduce HR-Bench, the first deliberately designed benchmark to rigorously evaluate MLLM performance on 4K & 8K images. Through extensive experiments, we demonstrate that while downsampling HR images leads to vision information loss, leveraging complementary modalities, e.g., text, can effectively compensate for this loss. Building upon this insight, we propose Divide, Conquer and Combine, a novel training-free framework for enhancing MLLM perception of HR images. Our method follows a three-staged approach: 1) Divide: recursively partitioning the HR image into patches and merging similar patches to minimize computational overhead, 2) Conquer: leveraging the MLLM to generate accurate textual descriptions for each image patch, and 3) Combine: utilizing the generated text descriptions to enhance the MLLM's understanding of the overall HR image. Extensive experiments show that: 1) the SOTA MLLM achieves 63% accuracy, which is markedly lower than the 87% accuracy achieved by humans on HR-Bench; 2) our method brings consistent and significant improvements (a relative increase of +6% on HR-Bench and +8% on general multimodal benchmarks).

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JMLR Journal 2025 Journal Article

FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion

Anke Tang
Li Shen
Yong Luo
Enneng Yang
Han Hu
Lefei Zhang
Bo Du
Dacheng Tao

Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness. We present FusionBench, the first benchmark and a unified library designed specifically for deep model fusion. Our benchmark consists of multiple tasks, each with different settings of models and datasets. This variety allows us to compare fusion methods across different scenarios and model scales. Additionally, FusionBench serves as a unified library for easy implementation and testing of new fusion techniques. FusionBench is open source and actively maintained, with community contributions encouraged. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2025. ( edit, beta )

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NeurIPS Conference 2025 Conference Paper

Merging on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging

Anke Tang
Enneng Yang
Li Shen
Yong Luo
Han Hu
Lefei Zhang
Bo Du
Dacheng Tao

Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their specialized capabilities across different tasks and domains. Current model merging techniques focus on merging all available models simultaneously, with weight interpolation-based methods being the predominant approach. However, these conventional approaches are not well-suited for scenarios where models become available sequentially, and they often suffer from high memory requirements and potential interference between tasks. In this study, we propose a training-free projection-based continual merging method that processes models sequentially through orthogonal projections of weight matrices and adaptive scaling mechanisms. Our method operates by projecting new parameter updates onto subspaces orthogonal to existing merged parameter updates while using an adaptive scaling mechanism to maintain stable parameter distances, enabling efficient sequential integration of task-specific knowledge. Our approach maintains constant memory complexity to the number of models, minimizes interference between tasks through orthogonal projections, and retains the performance of previously merged models through adaptive task vector scaling. Extensive experiments on CLIP-ViT models demonstrate that our method achieves a 5-8% average accuracy improvement while maintaining robust performance in different task orderings. Code is publicly available at https: //github. com/tanganke/opcm.

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NeurIPS Conference 2025 Conference Paper

MixPrompt: Efficient Mixed Prompting for Multimodal Semantic Segmentation

Zhiwei Hao
Zhongyu Xiao
Jianyuan Guo
Li Shen
Yong Luo
Han Hu
Dan Zeng

Recent advances in multimodal semantic segmentation show that incorporating auxiliary inputs—such as depth or thermal images—can significantly improve performance over single-modality (RGB-only) approaches. However, most existing solutions rely on parallel backbone networks and complex fusion modules, greatly increasing model size and computational demands. Inspired by prompt tuning in large language models, we introduce \textbf{MixPrompt}: a prompting-based framework that integrates auxiliary modalities into a pretrained RGB segmentation model without modifying its architecture. MixPrompt uses a lightweight prompting module to extract and fuse information from auxiliary inputs into the main RGB backbone. This module is initialized using the early layers of a pretrained RGB feature extractor, ensuring a strong starting point. At each backbone layer, MixPrompt aligns RGB and auxiliary features in multiple low-rank subspaces, maximizing information use with minimal parameter overhead. An information mixing scheme enables cross-subspace interaction for further performance gains. During training, only the prompting module and segmentation head are updated, keeping the RGB backbone frozen for parameter efficiency. Experiments across NYU Depth V2, SUN-RGBD, MFNet, and DELIVER datasets show that MixPrompt achieves improvements of 4. 3, 1. 1, 0. 4, and 1. 1 mIoU, respectively, over two-branch baselines, while using nearly half the parameters. MixPrompt also outperforms recent prompting-based methods under similar compute budgets.

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AAAI Conference 2025 Conference Paper

MTGA: Multi-View Temporal Granularity Aligned Aggregation for Event-Based Lip-Reading

Wenhao Zhang
Jun Wang
Yong Luo
Lei Yu
Wei Yu
Zheng He
Jialie Shen

Lip-reading is to utilize the visual information of the speaker’s lip movements to recognize words and sentences. Existing event-based lip-reading solutions integrate different frame rate branches to learn spatio-temporal features of varying granularities. However, aggregating events into event frames inevitably leads to the loss of fine-grained temporal information within frames. To remedy this drawback, we propose a novel framework termed Multi-view Temporal Granularity aligned Aggregation (MTGA). Specifically, we first present a novel event representation method, namely time-segmented voxel graph list, where the most significant local voxels are temporally connected into a graph list. Then we design a spatio-temporal fusion module based on temporal granularity alignment, where the global spatial features extracted from event frames, together with the local relative spatial and temporal features contained in voxel graph list are effectively aligned and integrated. Finally, we design a temporal aggregation module that incorporates positional encoding, which enables the capture of local absolute spatial and global temporal information. Experiments demonstrate that our method outperforms both the event-based and video-based lip-reading counterparts.

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IJCAI Conference 2025 Conference Paper

Open-Vocabulary Fine-Grained Hand Action Detection

Ting Zhe
Mengya Han
Xiaoshuai Hao
Yong Luo
Zheng He
Xiantao Cai
Jing Zhang

In this work, we address the new challenge of open-vocabulary fine-grained hand action detection, which aims to recognize hand actions from both known and novel categories using textual descriptions. Traditional hand action detection methods are limited to closed-set detection, making it difficult for them to generalize to new, unseen hand action categories. While current open-vocabulary detection (OVD) methods are effective at detecting novel objects, they face challenges with fine-grained action recognition, particularly when data is limited and heterogeneous. This often leads to poor generalization and performance bias between base and novel categories. To address these issues, we propose a novel approach, Open-FGHA (Open-vocabulary Fine-Grained Hand Action), which learns to distinguish fine-grained features across multiple modalities from limited heterogeneous data. It then identifies optimal matching relationships among these features, enabling accurate open-vocabulary fine-grained hand action detection. Specifically, we introduce three key components: Hierarchical Heterogeneous Low-Rank Adaptation, Bidirectional Selection and Fusion Mechanism, and Cross-Modality Query Generator. These components work in unison to enhance the alignment and fusion of multimodal fine-grained features. Extensive experiments demonstrate that Open-FGHA outperforms existing OVD methods, showing its strong potential for open-vocabulary hand action detection. The source code is available at OV-FGHAD.

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NeurIPS Conference 2025 Conference Paper

Self-Evolving Pseudo-Rehearsal for Catastrophic Forgetting with Task Similarity in LLMs

Jun Wang
Liang Ding
Shuai Wang
Hongyu Li
Yong Luo
Huangxuan Zhao
Han Hu
Bo Du

Continual learning for large language models (LLMs) demands a precise balance between $\textbf{plasticity}$ - the ability to absorb new tasks - and $\textbf{stability}$ - the preservation of previously learned knowledge. Conventional rehearsal methods, which replay stored examples, are limited by long-term data inaccessibility; earlier pseudo-rehearsal methods require additional generation modules, while self-synthesis approaches often generate samples that poorly align with real tasks, suffer from unstable outputs, and ignore task relationships. We present $\textbf{\textit{Self-Evolving Pseudo-Rehearsal for Catastrophic Forgetting with Task Similarity}}(\textbf{SERS})$, a lightweight framework that 1) decouples pseudo-input synthesis from label creation, using semantic masking and template guidance to produce diverse, task-relevant prompts without extra modules; 2) applies label self-evolution, blending base-model priors with fine-tuned outputs to prevent over-specialization; and 3) introduces a dynamic regularizer driven by the Wasserstein distance between task distributions, automatically relaxing or strengthening constraints in proportion to task similarity. Experiments across diverse tasks on different LLMs show that our SERS reduces forgetting by over 2\% points against strong pseudo-rehearsal baselines, by ensuring efficient data utilization and wisely transferring knowledge. The code will be released at https: //github. com/JerryWangJun/LLM_CL_SERS/.

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EAAI Journal 2025 Journal Article

Spatial-Temporal Attention-based Interaction-aware trajectory prediction for human-driven vehicles at freeway merging areas

Jiayan Shen
Xiang Wang
Wenjuan E.
Yong Luo
Yanyun Tao
Fangyu Feng

Freeway merging areas are characterized by complex interactions between vehicles, requiring precise coordination to ensure smooth traffic flow. In mixed traffic environments, trajectory prediction for manual-driven vehicles (MVs) in merging areas plays a pivotal role in the decision-making and motion planning of autonomous vehicles (AVs), providing a reliable safeguard against merging conflicts. However, existing methods often overlook the non-Euclidean nature of vehicle interactions and lack adaptive attention to temporal dependencies, limiting long-term prediction accuracy. This study proposes a Spatial-Temporal Attention-based Interaction-aware (STAI) trajectory prediction model, which leverages historical trajectories to forecast MV motion. STAI integrates a Graph Attention Network (GAT) for dynamic spatial interactions and a Long Short-Term Memory network (LSTM) with Temporal Attention (TA-LSTM) for capturing time-varying features, enabling extraction of critical spatiotemporal cues. Two contextual features—lane label and lane change force—are introduced to encode lane structure and quantify mandatory merging urgency. Experiments on the Next Generation Simulation (NGSIM) Interstate 80 dataset show that STAI achieves a Root Mean Square Error (RMSE) of 3. 75 m for 5-s predictions, representing a 15. 15 % improvement over the baseline (Convolutional Social Long Short-Term Memory). It also maintains efficient inference with 0. 055 s per sample. Error analyses in different traffic density scenarios, as well as experiments on other public datasets (NGSIM United States Route 101, Highway Drone) and local dataset, Suzhou Bay Bridge dataset, demonstrate the model generalization capability. By effectively modeling spatiotemporal interactions, the proposed method enhances trajectory prediction accuracy, contributing to safer autonomous driving in complex merging scenarios.

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NeurIPS Conference 2025 Conference Paper

Value-Guided Decision Transformer: A Unified Reinforcement Learning Framework for Online and Offline Settings

Hongling Zheng
Li Shen
Yong Luo
Deheng Ye
Shuhan Xu
Bo Du
Jialie Shen
Dacheng Tao

The Conditional Sequence Modeling (CSM) paradigm, benefiting from the transformer's powerful distribution modeling capabilities, has demonstrated considerable promise in Reinforcement Learning (RL) tasks. However, much of the work has focused on applying CSM to single online or offline settings, with the general architecture rarely explored. Additionally, existing methods primarily focus on deterministic trajectory modeling, overlooking the randomness of state transitions and the diversity of future trajectory distributions. Fortunately, value-based methods offer a viable solution for CSM, further bridging the potential gap between offline and online RL. In this paper, we propose Value-Guided Decision Transformer (VDT), which leverages value functions to perform advantage-weighting and behavior regularization on the Decision Transformer (DT), guiding the policy toward upper-bound optimal decisions during the offline training phase. In the online tuning phase, VDT further integrates value-based policy improvement with behavior cloning under the CSM architecture through limited interaction and data collection, achieving performance improvement within minimal timesteps. The predictive capability of value functions for future returns is also incorporated into the sampling process. Our method achieves competitive performance on various standard RL benchmarks, providing a feasible solution for developing CSM architectures in general scenarios. Code is available at here.

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AAAI Conference 2024 Conference Paper

Cycle Self-Refinement for Multi-Source Domain Adaptation

Chaoyang Zhou
Zengmao Wang
Bo Du
Yong Luo

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multiple source domains to the unlabeled target domain. In this paper, we propose a cycle self-refinement domain adaptation method, which progressively attempts to learn the dominant transferable knowledge in each source domain in a cycle manner. Specifically, several source-specific networks and a domain-ensemble network are adopted in the proposed method. The source-specific networks are adopted to provide the dominant transferable knowledge in each source domain for instance-level ensemble on predictions of the samples in target domain. Then these samples with high-confidence ensemble predictions are adopted to refine the domain-ensemble network. Meanwhile, to guide each source-specific network to learn more dominant transferable knowledge, we force the features of the target domain from the domain-ensemble network and the features of each source domain from the corresponding source-specific network to be aligned with their predictions from the corresponding networks. Thus the adaptation ability of source-specific networks and the domain-ensemble network can be improved progressively. Extensive experiments on Office-31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods for most tasks.

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NeurIPS Conference 2024 Conference Paper

Decomposed Prompt Decision Transformer for Efficient Unseen Task Generalization

Hongling Zheng
Li Shen
Yong Luo
Tongliang Liu
Jialie Shen
Dacheng Tao

Multi-task offline reinforcement learning aims to develop a unified policy for diverse tasks without requiring real-time interaction with the environment. Recent work explores sequence modeling, leveraging the scalability of the transformer architecture as a foundation for multi-task learning. Given the variations in task content and complexity, formulating policies becomes a challenging endeavor, requiring careful parameter sharing and adept management of conflicting gradients to extract rich cross-task knowledge from multiple tasks and transfer it to unseen tasks. In this paper, we propose the Decomposed Prompt Decision Transformer (DPDT) that adopts a two-stage paradigm to efficiently learn prompts for unseen tasks in a parameter-efficient manner. We incorporate parameters from pre-trained language models (PLMs) to initialize DPDT, thereby providing rich prior knowledge encoded in language models. During the decomposed prompt tuning phase, we learn both cross-task and task-specific prompts on training tasks to achieve prompt decomposition. In the test time adaptation phase, the cross-task prompt, serving as a good initialization, were further optimized on unseen tasks through test time adaptation, enhancing the model's performance on these tasks. Empirical evaluation on a series of Meta-RL benchmarks demonstrates the superiority of our approach. The project is available at https: //github. com/ruthless-man/DPDT.

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AAAI Conference 2024 Conference Paper

Decomposing Semantic Shifts for Composed Image Retrieval

Xingyu Yang
Daqing Liu
Heng Zhang
Yong Luo
Chaoyue Wang
Jing Zhang

Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image. However, most existing methods focus on the composition learning of text and reference images and oversimplify the text as a description, neglecting the inherent structure and the user's shifting intention of the texts. As a result, these methods typically take shortcuts that disregard the visual cue of the reference images. To address this issue, we reconsider the text as instructions and propose a Semantic Shift Network (SSN) that explicitly decomposes the semantic shifts into two steps: from the reference image to the visual prototype and from the visual prototype to the target image. Specifically, SSN explicitly decomposes the instructions into two components: degradation and upgradation, where the degradation is used to picture the visual prototype from the reference image, while the upgradation is used to enrich the visual prototype into the final representations to retrieve the desired target image. The experimental results show that the proposed SSN demonstrates a significant improvement of 5.42% and 1.37% on the CIRR and FashionIQ datasets, respectively, and establishes a new state-of-the-art performance. The code is available at https://github.com/starxing-yuu/SSN.

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IJCAI Conference 2024 Conference Paper

Eliminating the Cross-Domain Misalignment in Text-guided Image Inpainting

Muqi Huang
Chaoyue Wang
Yong Luo
Lefei Zhang

Text-guided image inpainting has rapidly garnered prominence as a task in user-directed image synthesis, aiming to complete the occluded image regions following the textual prompt provided. However, current methods usually grapple with issues arising from the disparity between low-level pixel data and high-level semantic descriptions, which results in inpainted sections not harmonizing with the original image (either structurally or texturally). In this study, we introduce a Structure-Aware Inpainting Learning scheme and an Asymmetric Cross Domain Attention to address these cross-domain misalignment challenges. The proposed structure-aware learning scheme employs features of an intermediate modality as structure guidance to bridge the gap between text information and low-level pixels. Meanwhile, asymmetric cross-domain attention enhances the texture consistency between inpainted and unmasked regions. Our experiments show exceptional performance on leading datasets such as MS-COCO and Open Images, surpassing state-of-the-art text-guided image inpainting methods. Code is released at: https: //github. com/MucciH/ECDM-inpainting

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IJCAI Conference 2024 Conference Paper

Joint Input and Output Coordination for Class-Incremental Learning

Shuai Wang
Yibing Zhan
Yong Luo
Han Hu
Wei Yu
Yonggang Wen
Dacheng Tao

Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, current strategies still do not 1) adequately address the class bias problem, and 2) alleviate the mutual interference between new and old tasks, and 3) consider the problem of class bias within tasks. In light of the above issues, we analyze the cause of class bias in incremental learning, as well as the drawbacks of existing approaches, and propose a joint input and output coordination (JIOC) mechanism to address these issues. This mechanism assigns different weights to different categories of data according to the gradient of the output score, and uses knowledge distillation (KD) to reduce the mutual interference between the outputs of old and new tasks. The proposed mechanism is general and flexible, and can be incorporated into different incremental learning approaches that use memory storage. Extensive experiments show that our mechanism can significantly improve their performance.

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NeurIPS Conference 2024 Conference Paper

MG-Net: Learn to Customize QAOA with Circuit Depth Awareness

Yang Qian
Xinbiao Wang
Yuxuan Du
Yong Luo
Dacheng Tao

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency.

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NeurIPS Conference 2024 Conference Paper

MMSite: A Multi-modal Framework for the Identification of Active Sites in Proteins

Song Ouyang
Huiyu Cai
Yong Luo
Kehua Su
Lefei Zhang
Bo Du

The accurate identification of active sites in proteins is essential for the advancement of life sciences and pharmaceutical development, as these sites are of critical importance for enzyme activity and drug design. Recent advancements in protein language models (PLMs), trained on extensive datasets of amino acid sequences, have significantly improved our understanding of proteins. However, compared to the abundant protein sequence data, functional annotations, especially precise per-residue annotations, are scarce, which limits the performance of PLMs. On the other hand, textual descriptions of proteins, which could be annotated by human experts or a pretrained protein sequence-to-text model, provide meaningful context that could assist in the functional annotations, such as the localization of active sites. This motivates us to construct a $\textbf{ProT}$ein-$\textbf{A}$ttribute text $\textbf{D}$ataset ($\textbf{ProTAD}$), comprising over 570, 000 pairs of protein sequences and multi-attribute textual descriptions. Based on this dataset, we propose $\textbf{MMSite}$, a multi-modal framework that improves the performance of PLMs to identify active sites by leveraging biomedical language models (BLMs). In particular, we incorporate manual prompting and design a MACross module to deal with the multi-attribute characteristics of textual descriptions. MMSite is a two-stage ("First Align, Then Fuse") framework: first aligns the textual modality with the sequential modality through soft-label alignment, and then identifies active sites via multi-modal fusion. Experimental results demonstrate that MMSite achieves state-of-the-art performance compared to existing protein representation learning methods. The dataset and code implementation are available at https: //github. com/Gift-OYS/MMSite.

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IJCAI Conference 2024 Conference Paper

Zero-shot Learning for Preclinical Drug Screening

Kun Li
Weiwei Liu
Yong Luo
Xiantao Cai
Jia Wu
Wenbin Hu

Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. This presents a challenge, rendering supervised deep learning methods unsuitable for such scenarios. In this paper, we propose a zero-shot learning solution for the DRP task in preclinical drug screening. Specifically, we propose a Multi-branch Multi-Source Domain Adaptation Test Enhancement Plug-in, called MSDA. MSDA can be seamlessly integrated with conventional DRP methods, learning invariant features from the prior response data of similar drugs to enhance real-time predictions of unlabeled compounds. The results of experiments on two large drug response datasets showed that MSDA efficiently predicts drug responses for novel compounds, leading to a general performance improvement of 5-10% in the preclinical drug screening phase. The significance of this solution resides in its potential to accelerate the drug discovery process, improve drug candidate assessment, and facilitate the success of drug discovery. The code is available at https: //github. com/DrugD/MSDA.

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AAAI Conference 2023 Conference Paper

FedABC: Targeting Fair Competition in Personalized Federated Learning

Dui Wang
Li Shen
Yong Luo
Han Hu
Kehua Su
Yonggang Wen
Dacheng Tao

Federated learning aims to collaboratively train models without accessing their client's local private data. The data may be Non-IID for different clients and thus resulting in poor performance. Recently, personalized federated learning (PFL) has achieved great success in handling Non-IID data by enforcing regularization in local optimization or improving the model aggregation scheme on the server. However, most of the PFL approaches do not take into account the unfair competition issue caused by the imbalanced data distribution and lack of positive samples for some classes in each client. To address this issue, we propose a novel and generic PFL framework termed Federated Averaging via Binary Classification, dubbed FedABC. In particular, we adopt the ``one-vs-all'' training strategy in each client to alleviate the unfair competition between classes by constructing a personalized binary classification problem for each class. This may aggravate the class imbalance challenge and thus a novel personalized binary classification loss that incorporates both the under-sampling and hard sample mining strategies is designed. Extensive experiments are conducted on two popular datasets under different settings, and the results demonstrate that our FedABC can significantly outperform the existing counterparts.

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NeurIPS Conference 2023 Conference Paper

Federated Learning with Manifold Regularization and Normalized Update Reaggregation

Xuming An
Li Shen
Han Hu
Yong Luo

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets. In FL, the model inconsistency caused by the local data heterogeneity across clients results in the near-orthogonality of client updates, which leads to the global update norm reduction and slows down the convergence. Most previous works focus on eliminating the difference of parameters (or gradients) between the local and global models, which may fail to reflect the model inconsistency due to the complex structure of the machine learning model and the Euclidean space's limitation in meaningful geometric representations. In this paper, we propose FedMRUR by adopting the manifold model fusion scheme and a new global optimizer to alleviate the negative impacts. Concretely, FedMRUR adopts a hyperbolic graph manifold regularizer enforcing the representations of the data in the local and global models are close to each other in a low-dimensional subspace. Because the machine learning model has the graph structure, the distance in hyperbolic space can reflect the model bias better than the Euclidean distance. In this way, FedMRUR exploits the manifold structures of the representations to significantly reduce the model inconsistency. FedMRUR also aggregates the client updates norms as the global update norm, which can appropriately enlarge each client's contribution to the global update, thereby mitigating the norm reduction introduced by the near-orthogonality of client updates. Furthermore, we theoretically prove that our algorithm can achieve a linear speedup property $\mathcal{O}(\frac{1}{\sqrt{SKT}})$ for non-convex setting under partial client participation, where $S$ is the participated clients number, $K$ is the local interval and $T$ is the total number of communication rounds. Experiments demonstrate that FedMRUR can achieve a new state-of-the-art (SOTA) accuracy with less communication.

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IJCAI Conference 2023 Conference Paper

Improving Heterogeneous Model Reuse by Density Estimation

Anke Tang
Yong Luo
Han Hu
Fengxiang He
Kehua Su
Bo Du
Yixin Chen
Dacheng Tao

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.

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TIST Journal 2022 Journal Article

Intrinsic Performance Influence-based Participant Contribution Estimation for Horizontal Federated Learning

Lin Zhang
Lixin Fan
Yong Luo
Ling-Yu Duan

The rapid development of modern artificial intelligence technique is mainly attributed to sufficient and high-quality data. However, in the data collection, personal privacy is at risk of being leaked. This issue can be addressed by federated learning, which is proposed to achieve efficient model training among multiple data providers without direct data access and aggregation. To encourage more parties owning high-quality data to participate in the federated learning, it is important to evaluate and reward the participant contribution in a reasonable, robust, and efficient manner. To achieve this goal, we propose a novel contribution estimation method: Intrinsic Performance Influence-based Contribution Estimation (IPICE). In particular, the class-level intrinsic performance influence is adopted as the contribution estimation criteria in IPICE, and a neural network is employed to exploit the non-linear relationship between the performance change and estimated contribution. Extensive experiments are conducted on various datasets, and the results demonstrate that IPICE is more accurate and stable than the counterpart in various data distribution settings. The computational complexity is significantly reduced in our IPICE, especially when a new party joins the federation. IPICE assigns small contributions to bad/garbage data and thus prevent them from participating and deteriorating the learning ecosystem.

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AAAI Conference 2022 Conference Paper

Resistance Training Using Prior Bias: Toward Unbiased Scene Graph Generation

Chao Chen
Yibing Zhan
Baosheng Yu
Liu Liu
Yong Luo
Bo Du

Scene Graph Generation (SGG) aims to build a structured representation of a scene using objects and pairwise relationships, which benefits downstream tasks. However, current SGG methods usually suffer from sub-optimal scene graph generation because of the long-tailed distribution of training data. To address this problem, we propose Resistance Training using Prior Bias (RTPB) for the scene graph generation. Specifically, RTPB uses a distributed-based prior bias to improve models’ detecting ability on less frequent relationships during training, thus improving the model generalizability on tail categories. In addition, to further explore the contextual information of objects and relationships, we design a contextual encoding backbone network, termed as Dual Transformer (DTrans). We perform extensive experiments on a very popular benchmark, VG150, to demonstrate the effectiveness of our method for the unbiased scene graph generation. In specific, our RTPB achieves an improvement of over 10% under the mean recall when applied to current SGG methods. Furthermore, DTrans with RTPB outperforms nearly all stateof-the-art methods with a large margin. Code is available at https: //github. com/ChCh1999/RTPB

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IJCAI Conference 2018 Conference Paper

Deep Discrete Prototype Multilabel Learning

Xiaobo Shen
Weiwei Liu
Yong Luo
Yew-Soon Ong
Ivor W. Tsang

kNN embedding methods, such as the state-of-the-art LM-kNN, have shown impressive results in multi-label learning. Unfortunately, these approaches suffer expensive computation and memory costs in large-scale settings. To fill this gap, this paper proposes a novel deep prototype compression, i. e. , DBPC for fast multi-label prediction. DBPC compresses the database into a small set of short discrete prototypes, and uses the prototypes for prediction. The benefit of DBPC comes from two aspects: 1) The number of distance comparisons are reduced in the prototype; 2) The distance computation cost is significantly decreased in the reduced space. We propose to jointly learn the deep latent subspace and discrete prototypes within one framework. The encoding and decoding neural networks are employed to make deep discrete prototypes well represent the instances and labels. Extensive experiments on several large-scale datasets demonstrate that DBPC achieves several orders of magnitude lower storage and prediction complexity than state-of-the-art multi-label methods, while achieving competitive accuracy.

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IJCAI Conference 2018 Conference Paper

Online Heterogeneous Transfer Metric Learning

Yong Luo
Tongliang Liu
Yonggang Wen
Dacheng Tao

Distance metric learning (DML) has been demonstrated to be successful and essential in diverse applications. Transfer metric learning (TML) can help DML in the target domain with limited label information by utilizing information from some related source domains. The heterogeneous TML (HTML), where the feature representations vary from the source to the target domain, is general and challenging. However, current HTML approaches are usually conducted in a batch manner and cannot handle sequential data. This motivates the proposed online HTML (OHTML) method. In particular, the distance metric in the source domain is pre-trained using some existing DML algorithms. To enable knowledge transfer, we assume there are large amounts of unlabeled corresponding data that have representations in both the source and target domains. By enforcing the distances (between these unlabeled samples) in the target domain to agree with those in the source domain under the manifold regularization theme, we learn an improved target metric. We formulate the problem in the online setting so that the optimization is efficient and the model can be adapted to new coming data. Experiments in diverse applications demonstrate both effectiveness and efficiency of the proposed method.

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AAAI Conference 2017 Conference Paper

Cost-Sensitive Feature Selection via F-Measure Optimization Reduction

Meng Liu
Chang Xu
Yong Luo
Chao Xu
Yonggang Wen
Dacheng Tao

Feature selection aims to select a small subset from the high-dimensional features which can lead to better learning performance, lower computational complexity, and better model readability. The class imbalance problem has been neglected by traditional feature selection methods, therefore the selected features will be biased towards the majority classes. Because of the superiority of F-measure to accuracy for imbalanced data, we propose to use F-measure as the performance measure for feature selection algorithms. As a pseudo-linear function, the optimization of F-measure can be achieved by minimizing the total costs. In this paper, we present a novel cost-sensitive feature selection (CSFS) method which optimizes F-measure instead of accuracy to take class imbalance issue into account. The features will be selected according to optimal F-measure classiﬁer after solving a series of cost-sensitive feature selection sub-problems. The features selected by our method will fully represent the characteristics of not only majority classes, but also minority classes. Extensive experimental results conducted on synthetic, multi-class and multi-label datasets validate the efﬁciency and signiﬁcance of our feature selection method.

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IJCAI Conference 2017 Conference Paper

Exploiting High-Order Information in Heterogeneous Multi-Task Feature Learning

Yong Luo
Dacheng Tao
Yonggang Wen

Multi-task feature learning (MTFL) aims to improve the generalization performance of multiple related learning tasks by sharing features between them. It has been successfully applied to many pattern recognition and biometric prediction problems. Most of current MTFL methods assume that different tasks exploit the same feature representation, and thus are not applicable to the scenarios where data are drawn from heterogeneous domains. Existing heterogeneous transfer learning (including multi-task learning) approaches handle multiple heterogeneous domains by usually learning feature transformations across different domains, but they ignore the high-order statistics (correlation information) which can only be discovered by simultaneously exploring all domains. We therefore develop a tensor based heterogeneous MTFL (THMTFL) framework to exploit such high-order information. Specifically, feature transformations of all domains are learned together, and finally used to derive new representations. A connection between all domains is built by using the transformations to project the pre-learned predictive structures of different domains into a common subspace, and minimizing their divergence in the subspace. By exploring the high-order information, the proposed THMTFL can obtain more reliable feature transformations compared with existing heterogeneous transfer learning approaches. Extensive experiments on both text categorization and social image annotation demonstrate superiority of the proposed method.

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IJCAI Conference 2017 Conference Paper

General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer

Yong Luo
Yonggang Wen
Tongliang Liu
Dacheng Tao

Transfer learning aims to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. Recently, transfer distance metric learning (TDML) has attracted lots of interests, but most of these methods assume that feature representations for the source and target learning tasks are the same. Hence, they are not suitable for the applications, in which the data are from heterogeneous domains (feature spaces, modalities and even semantics). Although some existing heterogeneous transfer learning (HTL) approaches is able to handle such domains, they lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. We therefore develop a general and flexible heterogeneous TDML (HTDML) framework based on the knowledge fragment transfer strategy. In the proposed HTDML, any (linear or nonlinear) distance metric learning algorithms can be employed to learn the source metric beforehand. Then a set of knowledge fragments are extracted from the pre-learned source metric to help target metric learning. In addition, either linear or nonlinear distance metric can be learned for the target domain. Extensive experiments on both scene classification and object recognition demonstrate superiority of the proposed method.

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IJCAI Conference 2016 Conference Paper

On Combining Side Information and Unlabeled Data for Heterogeneous Multi-Task Metric Learning

Yong Luo
Yonggang Wen
Dacheng Tao

Distance metric learning (DML) is critical for a wide variety of machine learning algorithms and pattern recognition applications. Transfer metric learning (TML) leverages the side information (e. g. , similar/dissimilar constraints over pairs of samples) from related domains to help the target metric learning (with limited information). Current TML tools usually assume that different domains exploit the same feature representation, and thus are not applicable to tasks where data are drawn from heterogeneous domains. Heterogeneous transfer learning approaches handle heterogeneous domains by usually learning feature transformations across different domains. The learned transformation can be used to derive a metric, but these approaches are mostly limited by their capability of only handling two domains. This motivates the proposed heterogeneous multi-task metric learning (HMTML) framework for handling multiple domains by combining side information and unlabeled data. Specifically, HMTML learns the metrics for all different domains simultaneously by maximizing their high-order correlation (parameterized by feature covariance of unlabeled data) in a common subspace, which is induced by the transformations derived from the metrics. Extensive experiments on both multi-language text categorization and multi-view social image annotation demonstrate the effectiveness of the proposed method.

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AAAI Conference 2015 Conference Paper

Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification

Meng Liu
Yong Luo
Dacheng Tao
Chao Xu
Yonggang Wen

Multi-label image classiﬁcation is of signiﬁcant interest due to its major role in real-world web image analysis applications such as large-scale image retrieval and browsing. Recently, matrix completion (MC) has been developed to deal with multi-label classiﬁcation tasks. MC has distinct advantages, such as robustness to missing entries in the feature and label spaces and a natural ability to handle multi-label problems. However, current MC-based multi-label image classi- ﬁcation methods only consider data represented by a singleview feature, therefore, do not precisely characterize images that contain several semantic concepts. An intuitive way to utilize multiple features taken from different views is to concatenate the different features into a long vector; however, this concatenation is prone to over-ﬁtting and leads to high time complexity in MC-based image classiﬁcation. Therefore, we present a novel multi-view learning model for MCbased image classiﬁcation, called low-rank multi-view matrix completion (lrMMC), which ﬁrst seeks a low-dimensional common representation of all views by utilizing the proposed low-rank multi-view learning (lrMVL) algorithm. In lrMVL, the common subspace is constrained to be low rank so that it is suitable for MC. In addition, combination weights are learned to explore complementarity between different views. An efﬁcient solver based on ﬁxed-point continuation (FPC) is developed for optimization, and the learned low-rank representation is then incorporated into MC-based image classiﬁcation. Extensive experimentation on the challenging PAS- CAL VOC’ 07 dataset demonstrates the superiority of lr- MMC compared to other multi-label image classiﬁcation approaches.

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AAAI Conference 2014 Conference Paper

Pre-Trained Multi-View Word Embedding Using Two-Side Neural Network

Yong Luo
Jian Tang
Jun Yan
Chao Xu
Zheng Chen

Word embedding aims to learn a continuous representation for each word. It attracts increasing attention due to its effectiveness in various tasks such as named entity recognition and language modeling. Most existing word embedding results are generally trained on one individual data source such as news pages or Wikipedia articles. However, when we apply them to other tasks such as web search, the performance suffers. To obtain a robust word embedding for different applications, multiple data sources could be leveraged. In this paper, we proposed a two-side multimodal neural network to learn a robust word embedding from multiple data sources including free text, user search queries and search click-through data. This framework takes the word embeddings learned from different data sources as pre-train, and then uses a two-side neural network to unify these embeddings. The pre-trained embeddings are obtained by adapting the recently proposed CBOW algorithm. Since the proposed neural network does not need to re-train word embeddings for a new task, it is highly scalable in real world problem solving. Besides, the network allows weighting different sources differently when applied to different application tasks. Experiments on two real-world applications including web search ranking and word similarity measuring show that our neural network with multiple sources outperforms state-of-the-art word embedding algorithm with each individual source. It also outperforms other competitive baselines using multiple sources.

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AAAI Conference 2013 Conference Paper

Vector-Valued Multi-View Semi-Supervsed Learning for Multi-Label Image Classification

Yong Luo
Dacheng Tao
Chang Xu
Dongchen Li
Chao Xu

Images are usually associated with multiple labels and comprised of multiple views, due to each image containing several objects (e. g. a pedestrian, bicycle and tree) and multiple visual features (e. g. color, texture and shape). Currently available tools tend to use either labels or features for classiﬁcation, but both are necessary to describe the image properly. There have been recent successes in using vector-valued functions, which construct matrix-valued kernels, to explore the multi-label structure in the output space. This has motivated us to develop multi-view vector-valued manifold regularization (MV3 MR) in order to integrate multiple features. MV3 MR exploits the complementary properties of different features, and discovers the intrinsic local geometry of the compact support shared by different features, under the theme of manifold regularization. We validate the effectiveness of the proposed MV3 MR methodology for image classiﬁcation by conducting extensive experiments on two challenge datasets, PASCAL VOC’ 07 and MIR Flickr.

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I&C Journal 2005 Journal Article

Transitivity in coercive subtyping

Zhaohui Luo
Yong Luo

Coercive subtyping is a general approach to abbreviation and subtyping in dependent type theories with inductive types. Coherence and admissibility of transitivity are important both for understanding of the framework and for its correct implementation. In this paper, we study the issue of transitivity in the context of subtyping for parameterised inductive types. In particular, we propose and study the notion of weak transitivity and show that, for a large class of parameterised inductive types, the natural subtyping rules are coherent and weak transitivity is admissible in an intensional type theory. A possible extension of type theory with certain extensional computation rules is also discussed for achieving admissibility of transitivity in general.

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