36 citations · 136 across the 25 of their papers we have counts for
23 papers · 1 filter
MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding
Yuan Wang, Di Huang, Yaqi Zhang +5
Generating lifelike human motions from descriptive texts has experienced remarkable research focus in the recent years, propelled by the emerging requirements of digital humans.Des…
Holistic-Motion2D: Scalable Whole-body Human Motion Generation in 2D Space
Yuan Wang, Zhao Wang, Junhao Gong +8
In this paper, we introduce a novel path to human motion generation by focusing on 2D space. Traditional methods have primarily generated human motions in 3D, wh…
A Comprehensive Survey on 3D Content Generation
Jian Liu, Xiaoshui Huang, Tianyu Huang +8
Recent years have witnessed remarkable advances in artificial intelligence generated content(AIGC), with diverse input modalities, e.g., text, image, video, audio and 3D. The 3D is…
Agent3D-Zero: An Agent for Zero-shot 3D Understanding
Sha Zhang, Di Huang, Jiajun Deng +4
The ability to understand and reason the 3D real world is a crucial milestone towards artificial general intelligence. The current common practice is to finetune Large Language Mod…
DetToolChain: A New Prompting Paradigm to Unleash Detection Ability of MLLM
Yixuan Wu, Yizhou Wang, Shixiang Tang +5
We present DetToolChain, a novel prompting paradigm, to unleash the zero-shot object detection ability of multimodal large language models (MLLMs), such as GPT-4V and Gemini. Our a…
Hulk: A Universal Knowledge Translator for Human-Centric Tasks
Yizhou Wang, Yixuan Wu, Weizhen He +8
Human-centric perception tasks, e.g., pedestrian detection, skeleton-based action recognition, and pose estimation, have wide industrial applications, such as metaverse and sports…
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving
Honghui Yang, Sha Zhang, Di Huang +9
In the context of autonomous driving, the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown w…
Relation-Aware Distribution Representation Network for Person Clustering with Multiple Modalities
Kaijian Liu, Shixiang Tang, Ziyue Li +4
Person clustering with multi-modal clues, including faces, bodies, and voices, is critical for various tasks, such as movie parsing and identity-based movie editing. Related method…
MotionGPT: Finetuned LLMs Are General-Purpose Motion Generators
Yaqi Zhang, Di Huang, Bin Liu +7
Generating realistic human motion from given action descriptions has experienced significant advancements because of the emerging requirement of digital humans. While recent works…
Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions
Weizhen He, Yiheng Deng, Shixiang Tang +9
Human intelligence can retrieve any person according to both visual and language descriptions. However, the current computer vision community studies specific person re-identificat…
HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining
Shixiang Tang, Cheng Chen, Qingsong Xie +9
Human-centric perceptions include a variety of vision tasks, which have widespread industrial applications, including surveillance, autonomous driving, and the metaverse. It is des…
UniHCP: A Unified Model for Human-Centric Perceptions
Yuanzheng Ci, Yizhou Wang, Meilin Chen +7
Human-centric perceptions (e.g., pose estimation, human parsing, pedestrian detection, person re-identification, etc.) play a key role in industrial applications of visual models.…
Saliency Guided Contrastive Learning on Scene Images
Meilin Chen, Yizhou Wang, Shixiang Tang +6
Self-supervised learning holds promise in leveraging large numbers of unlabeled data. However, its success heavily relies on the highly-curated dataset, e.g., ImageNet, which still…
Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains
Haiyang Yang, Meilin Chen, Yizhou Wang +5
Generalizing learned representations across significantly different visual domains is a fundamental yet crucial ability of the human visual system. While recent self-supervised lea…
Feature Erasing and Diffusion Network for Occluded Person Re-Identification
Zhikang Wang, Feng Zhu, Shixiang Tang +3
Occluded person re-identification (ReID) aims at matching occluded person images to holistic ones across different camera views. Target Pedestrians (TP) are usually disturbed by No…
Revisiting the Transferability of Supervised Pretraining: an MLP Perspective
Yizhou Wang, Shixiang Tang, Feng Zhu +4
The pretrain-finetune paradigm is a classical pipeline in visual learning. Recent progress on unsupervised pretraining methods shows superior transfer performance to their supervis…
Layerwise Optimization by Gradient Decomposition for Continual Learning
Shixiang Tang, Dapeng Chen, Jinguo Zhu +2
Deep neural networks achieve state-of-the-art and sometimes super-human performance across various domains. However, when learning tasks sequentially, the networks easily forget th…
Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification
Shijie Yu, Feng Zhu, Dapeng Chen +5
Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when un…
Complementary Relation Contrastive Distillation
Jinguo Zhu, Shixiang Tang, Dapeng Chen +5
Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation o…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Shixiang Tang, Peng Su, Dapeng Chen +1
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algori…
Gradient Regularized Contrastive Learning for Continual Domain Adaptation
Peng Su, Shixiang Tang, Peng Gao +3
Human beings can quickly adapt to environmental changes by leveraging learning experience. However, the poor ability of adapting to dynamic environments remains a major challenge f…
Continual Representation Learning for Biometric Identification
Bo Zhao, Shixiang Tang, Dapeng Chen +2
With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly i…
Adapting Object Detectors with Conditional Domain Normalization
Peng Su, Kun Wang, Xingyu Zeng +4
Real-world object detectors are often challenged by the domain gaps between different datasets. In this work, we present the Conditional Domain Normalization (CDN) to bridge the do…