arXiv Computer Vision
Aug 27

A Dual-Transformer for Multi-Camera View Recommendation

The paper introduces a Dual-Transformer architecture with Cross-Attention for multi-camera view recommendation, achieving a 56.60% Precision@0.5 on the TVMCE dataset, surpassing the previous best of 37.16%. The model separates temporal encoding of past frames from candidate view querying, and an ablation study shows the SwinV2 backbone yields 69.65% Precision@0.5. Fine‑tuning with as little as 20% of a target video improves precision, suggesting efficient personalization for specific editing styles.

By Josep Cabacas-Maso, Carles Ventura, Ismael Benito-Altamirano
arXiv AI
Sep 15

SGWIB:Sliced Gromov-Wasserstein Information Bottleneck for Video Highlight Detection

The paper introduces SGWIB, a single‑modal video highlight detection framework that applies an information‑bottleneck approach while preserving inter‑segment temporal structure through a new Sliced Gromov‑Monge Gap regularizer. It also proposes Home‑Away‑Related Contextual Pseudo‑Labels and a contextual disentanglement module to mitigate sports‑specific bias. Experiments on MrHiSum and MoSu datasets show SGWIB outperforms existing methods on multiple ranking and accuracy metrics.

By Hanjuan Huang, Yung-Chieh Yeh, Hsing-Kuo Pao
arXiv Computer Vision
6d ago

EviDETR: Preserving Query-Relevant Temporal Evidence for Moment Retrieval and Highlight Detection

EviDETR is a new framework for joint video moment retrieval and highlight detection that preserves query‑relevant temporal evidence throughout its pipeline. It introduces three key components: Semantic‑aware Feature Reweighting (SFR) to enhance clip representations, a Temporal Top‑2 Mixture‑of‑Experts (TTop2MoE) decoder for query‑adaptive refinement, and MR‑to‑HD (MR2HD) fusion to transfer retrieval evidence to highlight prediction. Using CLIP+SlowFast features, EviDETR achieves state‑of‑the‑art performance on QVHighlights and demonstrates strong cross‑dataset transferability on TACoS and Charades‑STA.

By Haoran Sun, Yufan Li, Qichen Zhang, Haoran Zhao, Shuqi Wang
arXiv AI
Jun 6

Seeing Time: Benchmarking Chronological Reasoning and Shortcut Biases in Vision-Language Models

arXiv:2606. 05702v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) have significantly enhanced their ability to interpret complex visual semantics, yet their capacity for chronological reasoning remains under-explored.

By Haoyu Zhou, Qing Qing, Caichong Li, Qixin Zhang, Yongcheng Jing, Ziqi Xu, Juncheng Hu, Xikun Zhang, Renqiang Luo