12 citations · 21 across the 16 of their papers we have counts for
17 papers
PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution
Yang Liu, Weixing Chen, Xinshuai Song +8
Vision-language-action models, world models, and agentic planners each advance physical intelligence, yet their composition lacks a common execution abstraction, shared state, sema…
RADAR: Benchmarking Vision-Language-Action Generalization via Real-World Dynamics, Spatial-Physical Intelligence, and Autonomous Evaluation
Yuhao Chen, Zhihao Zhan, Xiaoxin Lin +11
VLA models have achieved remarkable progress in embodied intelligence; however, their evaluation remains largely confined to simulations or highly constrained real-world settings.…
Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
Haijing Liu, Zhiyuan Song, Hefeng Wu +3
Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-pe…
Human-Centric Open-Future Task Discovery: Formulation, Benchmark, and Scalable Tree-Based Search
Zijian Song, Xiaoxin Lin, Tao Pu +3
Recent progress in robotics and embodied AI is largely driven by Large Multimodal Models (LMMs). However, a key challenge remains underexplored: how can we advance LMMs to discover…
DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +2
Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image, requiring both precise intra-class localization to pin…
Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +4
Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a wide range of visual concepts in…
Dynamic Correlation Learning and Regularization for Multi-Label Confidence Calibration
Tianshui Chen, Weihang Wang, Tao Pu +4
Modern visual recognition models often display overconfidence due to their reliance on complex deep neural networks and one-hot target supervision, resulting in unreliable confiden…
Spatial-Temporal Knowledge-Embedded Transformer for Video Scene Graph Generation
Tao Pu, Tianshui Chen, Hefeng Wu +2
Video scene graph generation (VidSGG) aims to identify objects in visual scenes and infer their relationships for a given video. It requires not only a comprehensive understanding…
Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels
Tao Pu, Qianru Lao, Hefeng Wu +2
As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other a…
Dual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +3
Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-s…
Heterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Tianshui Chen, Tao Pu, Lingbo Liu +3
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and th…
Semantic Representation and Dependency Learning for Multi-Label Image Recognition
Tao Pu, Mingzhan Sun, Hefeng Wu +3
Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing…
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +1
Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging…
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
Tianshui Chen, Tao Pu, Hefeng Wu +2
Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-s…
AU-Expression Knowledge Constrained Representation Learning for Facial Expression Recognition
Tao Pu, Tianshui Chen, Yuan Xie +2
Recognizing human emotion/expressions automatically is quite an expected ability for intelligent robotics, as it can promote better communication and cooperation with humans. Curre…
Adversarial Graph Representation Adaptation for Cross-Domain Facial Expression Recognition
Yuan Xie, Tianshui Chen, Tao Pu +2
Data inconsistency and bias are inevitable among different facial expression recognition (FER) datasets due to subjective annotating process and different collecting conditions. Re…
Cross-Domain Facial Expression Recognition: A Unified Evaluation Benchmark and Adversarial Graph Learning
Tianshui Chen, Tao Pu, Hefeng Wu +3
To address the problem of data inconsistencies among different facial expression recognition (FER) datasets, many cross-domain FER methods (CD-FERs) have been extensively devised i…