TRINITY: A Multi-Perspective Benchmark for Personal-Style Video Highlight Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
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.
Multi-camera systems are foundational to modern media production, and multi-camera editing is a critical task. This involves the proper selection of the appropriate camera view at each moment. In this...
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.
Video highlight detection aims to identify temporally important segments that capture the most informative or engaging events in a video. Reliable prediction therefore requires not only discriminative...
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.