4 citations · 8 across the 15 of their papers we have counts for
17 papers
SC-WM: A Self-Correcting World Model with Closed-Loop Feedback for Vision-and-Language Navigation in Continuous Environments
Xuan Yao, Yuze Zhu, Junyu Gao +2
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to make fine-grained navigation decisions under partial observability. However, most existing met…
Dual-level Adaptation for Multi-Object Tracking: Building Test-Time Calibration from Experience and Intuition
Wen Guo, Pengfei Zhao, Zongmeng Wang +2
Multiple Object Tracking (MOT) has long been a fundamental task in computer vision, with broad applications in various real-world scenarios. However, due to distribution shifts in…
HulluEdit: Single-Pass Evidence-Consistent Subspace Editing for Mitigating Hallucinations in Large Vision-Language Models
Yangguang Lin, Quan Fang, Yufei Li +3
Object hallucination in Large Vision-Language Models (LVLMs) significantly hinders their reliable deployment. Existing methods struggle to balance efficiency and accuracy: they oft…
Cross-Modal Dual-Causal Learning for Long-Term Action Recognition
Xu Shaowu, Jia Xibin, Gao Junyu +3
Long-term action recognition (LTAR) is challenging due to extended temporal spans with complex atomic action correlations and visual confounders. Although vision-language models (V…
NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environments
Xuan Yao, Junyu Gao, Changsheng Xu
Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires agents to execute sequential navigation actions in complex environments guided by natural language instr…
Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models
Mengyuan Chen, Junyu Gao, Changsheng Xu
A straightforward pipeline for zero-shot out-of-distribution (OOD) detection involves selecting potential OOD labels from an extensive semantic pool and then leveraging a pre-train…
Revisiting Essential and Nonessential Settings of Evidential Deep Learning
Mengyuan Chen, Junyu Gao, Changsheng Xu
Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that provides reliable predictive uncertainty in a single forward pass, attracting significant atten…
A Comprehensive Survey on Evidential Deep Learning and Its Applications
Junyu Gao, Mengyuan Chen, Liangyu Xiang +1
Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomou…
Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding
Shengkai Sun, Daizong Liu, Jianfeng Dong +5
Unsupervised pre-training has shown great success in skeleton-based action understanding recently. Existing works typically train separate modality-specific models, then integrate…
Fast-Slow Test-Time Adaptation for Online Vision-and-Language Navigation
Junyu Gao, Xuan Yao, Changsheng Xu
The ability to accurately comprehend natural language instructions and navigate to the target location is essential for an embodied agent. Such agents are typically required to exe…
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation
Junyu Gao, Xinhong Ma, Changsheng Xu
Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural networks, current UDA models are opaque and cannot provide promising explanations, limiting…
Multimodal Imbalance-Aware Gradient Modulation for Weakly-supervised Audio-Visual Video Parsing
Jie Fu, Junyu Gao, Changsheng Xu
Weakly-supervised audio-visual video parsing (WS-AVVP) aims to localize the temporal extents of audio, visual and audio-visual event instances as well as identify the corresponding…
Combating Data Imbalances in Federated Semi-supervised Learning with Dual Regulators
Sikai Bai, Shuaicheng Li, Weiming Zhuang +7
Federated learning has become a popular method to learn from decentralized heterogeneous data. Federated semi-supervised learning (FSSL) emerges to train models from a small fracti…
Learning Muti-expert Distribution Calibration for Long-tailed Video Classification
Yufan Hu, Junyu Gao, Changsheng Xu
Most existing state-of-the-art video classification methods assume that the training data obey a uniform distribution. However, video data in the real world typically exhibit an im…
Learning Commonsense-aware Moment-Text Alignment for Fast Video Temporal Grounding
Ziyue Wu, Junyu Gao, Shucheng Huang +1
Grounding temporal video segments described in natural language queries effectively and efficiently is a crucial capability needed in vision-and-language fields. In this paper, we…
Fine-grained Temporal Contrastive Learning for Weakly-supervised Temporal Action Localization
Junyu Gao, Mengyuan Chen, Changsheng Xu
We target at the task of weakly-supervised action localization (WSAL), where only video-level action labels are available during model training. Despite the recent progress, existi…
Weakly-Supervised Video Object Grounding via Causal Intervention
Wei Wang, Junyu Gao, Changsheng Xu
We target at the task of weakly-supervised video object grounding (WSVOG), where only video-sentence annotations are available during model learning. It aims to localize objects de…