1 citations · 1 across the 10 of their papers we have counts for
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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…
ProTA: Probabilistic Token Aggregation for Text-Video Retrieval
Han Fang, Xianghao Zang, Chao Ban +5
Text-video retrieval aims to find the most relevant cross-modal samples for a given query. Recent methods focus on modeling the whole spatial-temporal relations. However, since vid…