6 papers
Adaptive Evidential Learning for Temporal-Semantic Robustness in Moment Retrieval
Haojian Huang, Kaijing Ma, Jin Chen +8
In the domain of moment retrieval, accurately identifying temporal segments within videos based on natural language queries remains challenging. Traditional methods often employ pr…
Structure-Aware Prototype Guided Trusted Multi-View Classification
Haojian Huang, Jiahao Shi, Zhe Liu +4
Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inc…
Uncertainty-Guided Self-Questioning and Answering for Video-Language Alignment
Jin Chen, Kaijing Ma, Haojian Huang +4
The development of multi-modal models has been rapidly advancing, with some demonstrating remarkable capabilities. However, annotating video-text pairs remains expensive and insuff…
Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification
Haojian Huang, Chuanyu Qin, Zhe Liu +6
Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process.…
Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding
Kaijing Ma, Haojian Huang, Jin Chen +8
Existing Video Temporal Grounding (VTG) models excel in accuracy but often overlook open-world challenges posed by open-vocabulary queries and untrimmed videos. This leads to unrel…
Disentangle and denoise: Tackling context misalignment for video moment retrieval
Kaijing Ma, Han Fang, Xianghao Zang +7
Video Moment Retrieval, which aims to locate in-context video moments according to a natural language query, is an essential task for cross-modal grounding. Existing methods focus…