Author name cluster

Qi Chu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

14 papers

1 author row

AAAI Conference 2026 Conference Paper

Flora: Effortless Context Construction to Arbitrary Length and Scale

Tianxiang Chen
Zhentao Tan
Xiaofan Bo
Yue Wu
Tao Gong
Qi Chu
Jieping Ye

Effectively handling long contexts is challenging for Large Language Models (LLMs) due to the rarity of long texts, high computational demands, and substantial forgetting of short-context abilities. Recent approaches have attempted to construct long contexts for instruction tuning, but these methods often require LLMs or human interventions, which are both costly and limited in length and diversity. Also, the drop in short-context performances of present long-context LLMs remains significant. In this paper, we introduce Flora, an effortless (human/LLM-free) long-context construction strategy. Flora can markedly enhance the long-context performance of LLMs by arbitrarily assembling short instructions based on categories and instructing LLMs to generate responses based on long-context meta-instructions. This enables Flora to produce contexts of arbitrary length and scale with rich diversity, while only slightly compromising short-context performance. Experiments on Llama3-8B-Instruct and QwQ-32B show that LLMs enhanced by Flora excel in three long-context benchmarks while maintaining strong performances in short-context tasks.

PDF Details DOI

AAAI Conference 2026 Conference Paper

HyperSign: Hierarchical Hypergraph-based Co-occurrence Modeling for Sign Language Recognition and Translation

Qianren Guo
Yuehang Wang
Yongji Zhang
Qi Chu
Sen Liu
Yu Jiang

Effectively capturing co-occurrence signals, such as hand shapes, facial expressions, and body postures, is critical for semantic understanding in sign language recognition (SLR) and translation (SLT). Although skeleton data offer greater efficiency and robustness than RGB inputs, existing methods typically rely on pairwise graph structures, limiting their ability to model complex high-order interactions across body regions. To address this limitation, we propose HyperSign, a hierarchical hypergraph neural network that systematically captures high-order co-occurrence patterns among diverse body parts. The Co-occurrence Graph Perception Module jointly learns relational structures via three complementary pathways: (1) traditional graph convolutions for modeling physical joint connections, (2) dynamic geometric hypergraphs constructed via k-nearest neighbors to encode local spatial patterns, and (3) soft hypergraphs generated by learnable prototypes to reveal latent semantic associations. To further enhance structural modeling and semantic consistency, a Meta-Part Hypergraph Fusion Module abstracts feature streams from the hands, face, and body into unified hypergraph nodes, while leveraging empirically derived co-occurrence priors to model high-order cross-part dependencies. Moreover, an uncertainty-aware collaborative distillation mechanism guides the model to focus on critical body regions. Extensive experiments on standard SLR and SLT benchmarks (e.g., PHOENIX-2014, PHOENIX-2014T, and CSL-Daily) demonstrate that HyperSign not only outperforms existing skeleton-based approaches in both speed and accuracy but also achieves competitive or superior results compared to several state-of-the-art RGB-based methods across multiple evaluation metrics.

PDF Details DOI

AAAI Conference 2026 Conference Paper

MagicPaint: Operate Anything for Image Inpainting with Diffusion Model

Qinhong Yang
DongDong Chen
Qi Chu
Tao Gong
Qiankun Liu
Zhentao Tan
Xulin Li
Huamin Feng

Recent diffusion-based models have significantly improved inpainting quality. However, existing methods struggle with multi-task inpainting due to conflicting optimization objectives, and current datasets are typically limited to task-specific scenarios, hindering joint training. To address these challenges, we propose MagicPaint, a unified diffusion-based inpainting model that supports object addition, removal, and unconditional inpainting across both text and image modalities. MagicPaint semantically decouples operation types and target content by learnable tokens in MMToken Module, effectively reconciling conflicting optimization objectives and enabling robust multi-task, multi-modal inpainting. Besides, a novel inpainting paradigm named MagicMask, encodes operating intent directly into the mask and applies a mask loss for spatially precise supervision. In addition, existing inpainting datasets are insufficient for multi-task and multi-modal scenarios, limiting the capability of inpainting models. Thus, we further introduce a new dataset comprising 2.1M image tuples. It is dedicatedly designed to support diverse inpainting scenarios and significantly improves upon existing datasets, particularly in object removal. Through efforts from both model and data perspectives, MagicPaint enables users to operate anything—add, remove or inpaint content which is specified through either text or image modalities in a seamless and unified manner. Extensive experiments demonstrate that MagicPaint achieves state-of-the-art performance across three key tasks (i.e., text-guided addition, image-guided addition, and object removal) and produces outputs with superior visual consistency and contextual fidelity compared to existing methods.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Rethinking Masked Data Reconstruction Pretraining for Strong 3D Action Representation Learning

Tao Gong
Qi Chu
Bin Liu
Nenghai Yu

In 3D human action recognition, limited supervised data makes it challenging to fully tap into the modeling potential of powerful networks such as transformers. As a result, researchers have been actively investigating effective self-supervised pre-training strategies. For example, MAMP shows that instead of following the prevalent masked joint reconstruction, explicit masked motion reconstruction is key to the success of learning effective feature representation for 3D action recognition. However, we find that if we make a simple and effective change to the reconstructed target of masked joint reconstruction, masked joint reconstruction can achieve the same results as masked motion reconstruction. The devil is in the special characteristic of 3D skeleton data and the normalization process of training targets. We need to dig for all effective information of targets during normalization. Besides, considering that mask data reconstruction focuses more on learning local relations in input data for fulfilling the reconstruction task, instead of modeling the relation among samples, we further employ contrastive learning to learn more discriminative 3D action representations. We show that contrastive learning can consistently boost the performance of model pre-trained by masked joint prediction under various settings, especially in the semi-supervised setting that has a very limited number of labeled samples. Extensive experiments on NTU-60, NTU-120, and PKU-MMD datasets show that the proposed pre-training strategy achieves state-of-the-art results without bells and whistles.

PDF Details DOI

IJCAI Conference 2025 Conference Paper

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

Xulin Li
Yan Lu
Bin Liu
Jiaze Li
Qinhong Yang
Tao Gong
Qi Chu
Mang Ye

In real applications, person re-identification (ReID) expects to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets cannot meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 135k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans over an entire year and 270 volunteers were photographed on average 29. 1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.

PDF Details DOI

AAAI Conference 2025 Conference Paper

Training-free Open-Vocabulary Semantic Segmentation via Diverse Prototype Construction and Sub-region Matching

Xuanpu Zhao
Dianmo Sheng
Zhentao Tan
Zhiwei Zhao
Tao Gong
Qi Chu
Bin Liu
Nenghai Yu

Open-vocabulary semantic segmentation (OVSS) aims to segment images of arbitrary categories specified by class labels. While previous approaches relied on extensive image-text pairs or dense semantic annotations, recent training-free methods attempted to overcome these limitations by constructing semantic prototypes in the construction stage and image-to-image matching (i.e., prototype matching) during testing. However, these methods often struggle to effectively capture the visual characteristics of categories and fail to utilize local features during prototype matching. To deal with these problems, we propose a novel training-free framework for OVSS that constructs diverse prototypes and performs fine-grained sub-region matching. Specifically, our method leverages Large Language Models (LLMs) to guide support image generation by descriptions of different attributes of categories and employs coarse-fine clustering to obtain diverse and robust part-level prototypes in the construction stage. During testing, we propose a sub-region matching method, which assigns part-level prototypes to sub-regions utilizing optimal transport, to fully utilize local image features among part-level prototypes. Extensive experiments demonstrate the effectiveness of our method and show that our method achieves state-of-the-art performance, outperforming previous methods across five datasets.

PDF Details DOI

AAAI Conference 2024 Conference Paper

MotionGPT: Finetuned LLMs Are General-Purpose Motion Generators

Yaqi Zhang
Di Huang
Bin Liu
Shixiang Tang
Yan Lu
Lu Chen
Lei Bai
Qi Chu

Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans. While recent works have achieved impressive results in generating motion directly from textual action descriptions, they often support only a single modality of the control signal, which limits their application in the real digital human industry. This paper presents a Motion General-Purpose generaTor (MotionGPT) that can use multimodal control signals, e.g., text and single-frame poses, for generating consecutive human motions by treating multimodal signals as special input tokens in large language models (LLMs). Specifically, we first quantize multimodal control signals into discrete codes and then formulate them in a unified prompt instruction to ask the LLMs to generate the motion answer. Our MotionGPT demonstrates a unified human motion generation model with multimodal control signals by tuning a mere 0.4% of LLM parameters. To the best of our knowledge, MotionGPT is the first method to generate human motion by multimodal control signals, which we hope can shed light on this new direction. Visit our webpage at https://qiqiapink.github.io/MotionGPT/.

PDF Details DOI

AAAI Conference 2024 Conference Paper

TCI-Former: Thermal Conduction-Inspired Transformer for Infrared Small Target Detection

Tianxiang Chen
Zhentao Tan
Qi Chu
Yue Wu
Bin Liu
Nenghai Yu

Infrared small target detection (ISTD) is critical to national security and has been extensively applied in military areas. ISTD aims to segment small target pixels from background. Most ISTD networks focus on designing feature extraction blocks or feature fusion modules, but rarely describe the ISTD process from the feature map evolution perspective. In the ISTD process, the network attention gradually shifts towards target areas. We abstract this process as the directional movement of feature map pixels to target areas through convolution, pooling and interactions with surrounding pixels, which can be analogous to the movement of thermal particles constrained by surrounding variables and particles. In light of this analogy, we propose Thermal Conduction-Inspired Transformer (TCI-Former) based on the theoretical principles of thermal conduction. According to thermal conduction differential equation in heat dynamics, we derive the pixel movement differential equation (PMDE) in the image domain and further develop two modules: Thermal Conduction-Inspired Attention (TCIA) and Thermal Conduction Boundary Module (TCBM). TCIA incorporates finite difference method with PMDE to reach a numerical approximation so that target body features can be extracted. To further remove errors in boundary areas, TCBM is designed and supervised by boundary masks to refine target body features with fine boundary details. Experiments on IRSTD-1k and NUAA-SIRST demonstrate the superiority of our method.

PDF Details DOI

AAAI Conference 2024 Conference Paper

Unifying Multi-Modal Uncertainty Modeling and Semantic Alignment for Text-to-Image Person Re-identification

Zhiwei Zhao
Bin Liu
Yan Lu
Qi Chu
Nenghai Yu

Text-to-Image person re-identification (TI-ReID) aims to retrieve the images of target identity according to the given textual description. The existing methods in TI-ReID focus on aligning the visual and textual modalities through contrastive feature alignment or reconstructive masked language modeling (MLM). However, these methods parameterize the image/text instances as deterministic embeddings and do not explicitly consider the inherent uncertainty in pedestrian images and their textual descriptions, leading to limited image-text relationship expression and semantic alignment. To address the above problem, in this paper, we propose a novel method that unifies multi-modal uncertainty modeling and semantic alignment for TI-ReID. Specifically, we model the image and textual feature vectors of pedestrian as Gaussian distributions, where the multi-granularity uncertainty of the distribution is estimated by incorporating batch-level and identity-level feature variances for each modality. The multi-modal uncertainty modeling acts as a feature augmentation and provides richer image-text semantic relationship. Then we present a bi-directional cross-modal circle loss to more effectively align the probabilistic features between image and text in a self-paced manner. To further promote more comprehensive image-text semantic alignment, we design a task that complements the masked language modeling, focusing on the cross-modality semantic recovery of global masked token after cross-modal interaction. Extensive experiments conducted on three TI-ReID datasets highlight the effectiveness and superiority of our method over state-of-the-arts.

PDF Details DOI

IJCAI Conference 2023 Conference Paper

Fluid Dynamics-Inspired Network for Infrared Small Target Detection

Tianxiang Chen
Qi Chu
Bin Liu
Nenghai Yu

Most infrared small target detection (ISTD) networks focus on building effective neural blocks or feature fusion modules but none describes the ISTD process from the image evolution perspective. The directional evolution of image pixels influenced by convolution, pooling and surrounding pixels is analogous to the movement of fluid elements constrained by surrounding variables ang particles. Inspired by this, we explore a novel research routine by abstracting the movement of pixels in the ISTD process as the flow of fluid in fluid dynamics (FD). Specifically, a new Fluid Dynamics-Inspired Network (FDI-Net) is devised for ISTD. Based on Taylor Central Difference (TCD) method, the TCD feature extraction block is designed, where convolution and Transformer structures are combined for local and global information. The pixel motion equation during the ISTD process is derived from the Navier–Stokes (N-S) equation, constructing a N-S Refinement Module that refines extracted features with edge details. Thus, the TCD feature extraction block determines the primary movement direction of pixels during detection, while the N-S Refinement Module corrects some skewed directions of the pixel stream to supplement the edge details. Experiments on IRSTD-1k and SIRST demonstrate that our method achieves SOTA performance in terms of evaluation metrics.

PDF Details DOI

AAAI Conference 2021 Conference Paper

Joint Color-irrelevant Consistency Learning and Identity-aware Modality Adaptation for Visible-infrared Cross Modality Person Re-identification

Zhiwei Zhao
Bin Liu
Qi Chu
Yan Lu
Nenghai Yu

Visible-infrared cross modality person re-identification (VI- ReID) is a core but challenging technology in the 24-hours intelligent surveillance system. How to eliminate the large modality gap lies in the heart of VI-ReID. Conventional methods mainly focus on directly aligning the heterogeneous modalities into the same space. However, due to the unbalanced color information between the visible and infrared images, the features of visible images tend to overfit the clothing color information, which would be harmful to the modality alignment. Besides, these methods mainly align the heterogeneous feature distributions in dataset-level while ignoring the valuable identity information, which may cause the feature misalignment of some identities and weaken the discrimination of features. To tackle above problems, we propose a novel approach for VI-ReID. It learns the colorirrelevant features through the color-irrelevant consistency learning (CICL) and aligns the identity-level feature distributions by the identity-aware modality adaptation (IAMA). The CICL and IAMA are integrated into a joint learning framework and can promote each other. Extensive experiments on two popular datasets SYSU-MM01 and RegDB demonstrate the superiority and effectiveness of our approach against the state-of-the-art methods.

PDF Details

AAAI Conference 2021 Conference Paper

Temporal ROI Align for Video Object Recognition

Tao Gong
Kai Chen
Xinjiang Wang
Qi Chu
Feng Zhu
Dahua Lin
Nenghai Yu
Huamin Feng

Video object detection is challenging in the presence of appearance deterioration in certain video frames. Therefore, it is a natural choice to aggregate temporal information from other frames of the same video into the current frame. However, ROI Align, as one of the most core procedures of video detectors, still remains extracting features from a single-frame feature map for proposals, making the extracted ROI features lack temporal information from videos. In this work, considering the features of the same object instance are highly similar among frames in a video, a novel Temporal ROI Align operator is proposed to extract features from other frames feature maps for current frame proposals by utilizing feature similarity. The proposed Temporal ROI Align operator can extract temporal information from the entire video for proposals. We integrate it into single-frame video detectors and other state-of-the-art video detectors, and conduct quantitative experiments to demonstrate that the proposed Temporal ROI Align operator can consistently and significantly boost the performance. Besides, the proposed Temporal ROI Align can also be applied into video instance segmentation.

PDF Details

AAAI Conference 2020 Conference Paper

DASOT: A Unified Framework Integrating Data Association and Single Object Tracking for Online Multi-Object Tracking

Qi Chu
Wanli Ouyang
Bin Liu
Feng Zhu
Nenghai Yu

In this paper, we propose an online multi-object tracking (MOT) approach that integrates data association and single object tracking (SOT) with a uniﬁed convolutional network (ConvNet), named DASOTNet. The intuition behind integrating data association and SOT is that they can complement each other. Following Siamese network architecture, DASOT- Net consists of the shared feature ConvNet, the data association branch and the SOT branch. Data association is treated as a special re-identiﬁcation task and solved by learning discriminative features for different targets in the data association branch. To handle the problem that the computational cost of SOT grows intolerably as the number of tracked objects increases, we propose an efﬁcient two-stage tracking method in the SOT branch, which utilizes the merits of correlation features and can simultaneously track all the existing targets within one forward propagation. With feature sharing and the interaction between them, data association branch and the SOT branch learn to better complement each other. Using a multi-task objective, the whole network can be trained endto-end. Compared with state-of-the-art online MOT methods, our method is much faster while maintaining a comparable performance.

PDF Details

IJCAI Conference 2020 Conference Paper

GSM: Graph Similarity Model for Multi-Object Tracking

Qiankun Liu
Qi Chu
Bin Liu
Nenghai Yu

The popular tracking-by-detection paradigm for multi-object tracking (MOT) focuses on solving data association problem, of which a robust similarity model lies in the heart. Most previous works make effort to improve feature representation for individual object while leaving the relations among objects less explored, which may be problematic in some complex scenarios. In this paper, we focus on leveraging the relations among objects to improve robustness of the similarity model. To this end, we propose a novel graph representation that takes both the feature of individual object and the relations among objects into consideration. Besides, a graph matching module is specially designed for the proposed graph representation to alleviate the impact of unreliable relations. With the help of the graph representation and the graph matching module, the proposed graph similarity model, named GSM, is more robust to the occlusion and the targets sharing similar appearance. We conduct extensive experiments on challenging MOT benchmarks and the experimental results demonstrate the effectiveness of the proposed method.

PDF Details DOI