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Zhiping Cai

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14 papers

2 author rows

AAAI Conference 2026 Conference Paper

TOSC: Task-Oriented Shape Completion for Open-World Dexterous Grasp Generation from Partial Point Clouds

Weishang Wu
Yifei Shi
Zhiping Cai

Task-oriented dexterous grasping remains challenging in robotic manipulations of open-world objects under severe partial observation, where significant missing data invalidates generic shape completion. In this paper, to overcome this limitation, we study \emph{Task-Oriented Shape Completion}, a new task that focuses on completing the potential contact regions rather than the entire shape. We argue that shape completion for grasping should be explicitly guided by the downstream manipulation task. To achieve this, we first generate multiple task-oriented shape completion candidates by leveraging the zero-shot capabilities of object functional understanding from several pre-trained foundation models. A 3D discriminative autoencoder is then proposed to evaluate the plausibility of each generated candidate and optimize the most plausible one from a global perspective. A conditional flow-matching model named FlowGrasp is developed to generate task-oriented dexterous grasps from the optimized shape. Our method achieves state-of-the-art performance in task-oriented dexterous grasping and task-oriented shape completion, improving the Grasp Displacement and the Chamfer Distance over the state-of-the-art by 16.17% and 55.26%, respectively. In particular, it shows good capabilities in grasping objects with severe missing data. It also demonstrates good generality in handling open-set categories and tasks.

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AAAI Conference 2025 Conference Paper

ALLVB: All-in-One Long Video Understanding Benchmark

Xichen Tan
Yuanjing Luo
Yunfan Ye
Fang Liu
Zhiping Cai

From image to video understanding, the capabilities of Multi-modal LLMs (MLLMs) are increasingly powerful. However, most existing video understanding benchmarks are relatively short, which makes them inadequate for effectively evaluating the long-sequence modeling capabilities of MLLMs. This highlights the urgent need for a comprehensive and integrated long video understanding benchmark to assess the ability of MLLMs thoroughly. To this end, we propose ALLVB (ALL-in-One Long Video Understanding Benchmark). ALLVB's main contributions include: 1) It integrates 9 major video understanding tasks. These tasks are converted into video QA formats, allowing a single benchmark to evaluate 9 different video understanding capabilities of MLLMs, highlighting the versatility, comprehensiveness, and challenging nature of ALLVB. 2) A fully automated annotation pipeline using GPT-4o is designed, requiring only human quality control, which facilitates the maintenance and expansion of the benchmark. 3) It contains 1,376 videos across 16 categories, averaging nearly 2 hours each, with a total of 252k QAs. To the best of our knowledge, it is the largest long video understanding benchmark in terms of the number of videos, average duration, and number of QAs. We have tested various mainstream MLLMs on ALLVB, and the results indicate that even the most advanced commercial models have significant room for improvement. This reflects the benchmark's challenging nature and demonstrates the substantial potential for development in long video understanding.

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ICML Conference 2025 Conference Paper

Does One-shot Give the Best Shot? Mitigating Model Inconsistency in One-shot Federated Learning

Hui Zeng
Wenke Huang 0003
Tongqing Zhou
Xinyi Wu
Guancheng Wan
Yingwen Chen 0001
Zhiping Cai

Turning the multi-round vanilla Federated Learning into one-shot FL (OFL) significantly reduces the communication burden and makes a big leap toward practical deployment. However, this work empirically and theoretically unravels that existing OFL falls into a garbage (inconsistent one-shot local models) in and garbage (degraded global model) out pitfall. The inconsistency manifests as divergent feature representations and sample predictions. This work presents a novel OFL framework FAFI that enhances the one-shot training on the client side to essentially overcome inferior local uploading. Specifically, unsupervised feature alignment and category-wise prototype learning are adopted for clients’ local training to be consistent in representing local samples. On this basis, FAFI uses informativeness-aware feature fusion and prototype aggregation for global inference. Extensive experiments on three datasets demonstrate the effectiveness of FAFI, which facilitates superior performance compared with 11 OFL baselines (+10. 86% accuracy). Code available at https: //github. com/zenghui9977/FAFI_ICML25

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AAAI Conference 2025 Conference Paper

Spatiotemporal-Aware Neural Fields for Dynamic CT Reconstruction

Qingyang Zhou
Yunfan Ye
Zhiping Cai

We propose a dynamic Computed Tomography (CT) reconstruction framework called STNF4D (SpatioTemporal-aware Neural Fields). First, we represent the 4D scene using four orthogonal volumes and compress these volumes into more compact hash grids. Compared to the plane decomposition method, this method enhances the model's capacity while keeping the representation compact and efficient. However, in densely predicted high-resolution dynamic CT scenes, the lack of constraints and hash conflicts in the hash grid features lead to obvious dot-like artifact and blurring in the reconstructed images. To address these issues, we propose the Spatiotemporal Transformer (ST-Former) that guides the model in selecting and optimizing features by sensing the spatiotemporal information in different hash grids, significantly improving the quality of reconstructed images. We conducted experiments on medical and industrial datasets covering various motion types, sampling modes, and reconstruction resolutions. Experimental results show that our method outperforms the second-best by 5.99 dB and 4.11 dB in medical and industrial scenes, respectively.

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AAAI Conference 2024 Conference Paper

Deep clustering with attribute-missing graphs, where only a subset of nodes possesses complete attributes while those of others are missing, is an important yet challenging topic in various practical applications. It has become a prevalent learning paradigm in existing studies to perform data imputation first and subsequently conduct clustering using the imputed information. However, these ``two-stage" methods disconnect the clustering and imputation processes, preventing the model from effectively learning clustering-friendly graph embedding. Furthermore, they are not tailored for clustering tasks, leading to inferior clustering results. To solve these issues, we propose a novel Attribute-Missing Graph Clustering (AMGC) method to alternately promote clustering and imputation in a unified framework, where we iteratively produce the clustering-enhanced nearest neighbor information to conduct the data imputation process and utilize the imputed information to implicitly refine the clustering distribution through model optimization. Specifically, in the imputation step, we take the learned clustering information as imputation prompts to help each attribute-missing sample gather highly correlated features within its clusters for data completion, such that the intra-class compactness can be improved. Moreover, to support reliable clustering, we maximize inter-class separability by conducting cost-efficient dual non-contrastive learning over the imputed latent features, which in turn promotes greater graph encoding capability for clustering sub-network. Extensive experiments on five datasets have verified the superiority of AMGC against competitors.

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AAAI Conference 2024 Conference Paper

DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection

Yunfan Ye
Kai Xu
Yuhang Huang
Renjiao Yi
Zhiping Cai

Limited by the encoder-decoder architecture, learning-based edge detectors usually have difficulty predicting edge maps that satisfy both correctness and crispness. With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size. Therefore, we propose the first diffusion model for the task of general edge detection, which we call DiffusionEdge. To avoid expensive computational resources while retaining the final performance, we apply DPM in the latent space and enable the classic cross-entropy loss which is uncertainty-aware in pixel level to directly optimize the parameters in latent space in a distillation manner. We also adopt a decoupled architecture to speed up the denoising process and propose a corresponding adaptive Fourier filter to adjust the latent features of specific frequencies. With all the technical designs, DiffusionEdge can be stably trained with limited resources, predicting crisp and accurate edge maps with much fewer augmentation strategies. Extensive experiments on four edge detection benchmarks demonstrate the superiority of DiffusionEdge both in correctness and crispness. On the NYUDv2 dataset, compared to the second best, we increase the ODS, OIS (without post-processing) and AC by 30.2%, 28.1% and 65.1%, respectively. Code: https://github.com/GuHuangAI/DiffusionEdge.

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AAAI Conference 2024 Conference Paper

Text-Based Occluded Person Re-identification via Multi-Granularity Contrastive Consistency Learning

Xinyi Wu
Wentao Ma
Dan Guo
Tongqing Zhou
Shan Zhao
Zhiping Cai

Text-based Person Re-identification (T-ReID), which aims at retrieving a specific pedestrian image from a collection of images via text-based information, has received significant attention. However, previous research has overlooked a challenging yet practical form of T-ReID: dealing with image galleries mixed with occluded and inconsistent personal visuals, instead of ideal visuals with a full-body and clear view. Its major challenges lay in the insufficiency of benchmark datasets and the enlarged semantic gap incurred by arbitrary occlusions and modality gap between text description and visual representation of the target person. To alleviate these issues, we first design an Occlusion Generator (OGor) for the automatic generation of artificial occluded images from generic surveillance images. Then, a fine-granularity token selection mechanism is proposed to minimize the negative impact of occlusion for robust feature learning, and a novel multi-granularity contrastive consistency alignment framework is designed to leverage intra-/inter-granularity of visual-text representations for semantic alignment of occluded visuals and query texts. Experimental results demonstrate that our method exhibits superior performance. We believe this work could inspire the community to investigate more dedicated designs for implementing T-ReID in real-world scenarios. The source code is available at https://github.com/littlexinyi/MGCC.

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IJCAI Conference 2022 Conference Paper

Initializing Then Refining: A Simple Graph Attribute Imputation Network

Wenxuan Tu
Sihang Zhou
Xinwang Liu
Yue Liu
Zhiping Cai
En Zhu
Changwang Zhang
Jieren Cheng

Representation learning on the attribute-missing graphs, whose connection information is complete while the attribute information of some nodes is missing, is an important yet challenging task. To impute the missing attributes, existing methods isolate the learning processes of attribute and structure information embeddings, and force both resultant representations to align with a common in-discriminative normal distribution, leading to inaccurate imputation. To tackle these issues, we propose a novel graph-oriented imputation framework called initializing then refining (ITR), where we first employ the structure information for initial imputation, and then leverage observed attribute and structure information to adaptively refine the imputed latent variables. Specifically, we first adopt the structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by aggregating the reliable and informative embeddings of attribute-observed samples according to the affinity structure. Specially, in our refining process, the affinity structure is adaptively updated through iterations by calculating the sample-wise correlations upon the recomposed embeddings. Extensive experiments on four benchmark datasets verify the superiority of ITR against state-of-the-art methods.

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AAAI Conference 2021 Conference Paper

Deep Fusion Clustering Network

Wenxuan Tu
Sihang Zhou
Xinwang Liu
Xifeng Guo
Zhiping Cai
En Zhu
Jieren Cheng

Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i. e. , “groundtruth” soft labels) generation. To tackle the above issues, we propose a Deep Fusion Clustering Network (DFCN). Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning. Also, a reliable target distribution generation measure and a triplet self-supervision strategy, which facilitate cross-modality information exploitation, are designed for network training. Extensive experiments on six benchmark datasets have demonstrated that the proposed DFCN consistently outperforms the state-of-the-art deep clustering methods. Our code is publicly available at https: //github. com/WxTu/DFCN.

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AAAI Conference 2021 Conference Paper

Dynamic Modeling Cross- and Self-Lattice Attention Network for Chinese NER

Shan Zhao
Minghao Hu
Zhiping Cai
Haiwen Chen
Fang Liu

Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encoding to integrate word-character lattice information, but fail to capture fine-grained correlations in word-character spaces. In this paper, we propose DC- SAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. By carefully combining cross-lattice and self-lattice attention modules with gated word-character semantic fusion unit, the network can explicitly capture fine-grained correlations across different spaces (e. g. , word-to-character and character-to-character), thus significantly improving model performance. Experiments on four Chinese NER datasets show that DCSAN obtains stateof-the-art results as well as efficiency compared to several competitive approaches.

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IJCAI Conference 2020 Conference Paper

Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction

Shan Zhao
Minghao Hu
Zhiping Cai
Fang Liu

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods such as label-feature concatenation to perform coarse-grained semantic fusion among cross-modal instances, but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this paper, we propose a deep Cross-Modal Attention Network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g. , token-to-token and labelto-token). Experiment results on CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 90. 62% F1 on entity recognition and 72. 97% F1 on relation classification. In ADE dataset, our model surpasses existing approaches by more than 1. 9% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

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TIST Journal 2019 Journal Article

Edge-enabled Disaster Rescue

Fang Liu
Yeting Guo
Zhiping Cai
Nong Xiao
Ziming Zhao

In the aftermath of earthquakes, floods, and other disasters, photos are increasingly playing more significant roles, such as finding missing people and assessing disasters, in rescue and recovery efforts. These disaster photos are taken in real time by the crowd, unmanned aerial vehicles, and wireless sensors. However, communications equipment is often damaged in disasters, and the very limited communication bandwidth restricts the upload of photos to the cloud center, seriously impeding disaster rescue endeavors. Based on edge computing, we propose Echo, a highly time-efficient disaster rescue framework. By utilizing the computing, storage, and communication abilities of edge servers, disaster photos are preprocessed and analyzed in real time, and more specific visuals are immensely helpful for conducting emergency response and rescue. This article takes the search for missing people as a case study to show that Echo can be more advantageous in terms of disaster rescue. To greatly conserve valuable communication bandwidth, only significantly associated images are extracted and uploaded to the cloud center for subsequent facial recognition. Furthermore, an adaptive photo detector is designed to utilize the precious and unstable communication bandwidth effectively, as well as ensure the photo detection precision and recall rate. The effectiveness and efficiency of the proposed method are demonstrated by simulation experiments.

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IJCAI Conference 2018 Conference Paper

Localized Incomplete Multiple Kernel k-means

Xinzhong Zhu
Xinwang Liu
Miaomiao Li
En Zhu
Li Liu
Zhiping Cai
Jianping Yin
Wen Gao

The recently proposed multiple kernel k-means with incomplete kernels (MKKM-IK) optimally integrates a group of pre-specified incomplete kernel matrices to improve clustering performance. Though it demonstrates promising performance in various applications, we observe that it does not \emph{sufficiently consider the local structure among data and indiscriminately forces all pairwise sample similarity to equally align with their ideal similarity values}. This could make the incomplete kernels less effectively imputed, and in turn adversely affect the clustering performance. In this paper, we propose a novel localized incomplete multiple kernel k-means (LI-MKKM) algorithm to address this issue. Different from existing MKKM-IK, LI-MKKM only requires the similarity of a sample to its k-nearest neighbors to align with their ideal similarity values. This helps the clustering algorithm to focus on closer sample pairs that shall stay together and avoids involving unreliable similarity evaluation for farther sample pairs. We carefully design a three-step iterative algorithm to solve the resultant optimization problem and theoretically prove its convergence. Comprehensive experiments on eight benchmark datasets demonstrate that our algorithm significantly outperforms the state-of-the-art comparable algorithms proposed in the recent literature, verifying the advantage of considering local structure.

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IS Journal 2013 Journal Article

Extreme Learning Machines [Trends & Controversies]

Erik Cambria
Guang-Bin Huang
Liyanaarachchi Lekamalage Chamara Kasun
Hongming Zhou
Chi Man Vong
Jiarun Lin
Jianping Yin
Zhiping Cai

This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data, " Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing, " Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C. M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing, " Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs, " Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections, " Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text, " Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures, " Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System, " Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction.

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