arXiv Computer Vision
TSMD: Temporal-Stream Modality Dropout for Robust Video Highlight Detection
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
3d ago
Spatial-Temporal Multi-scale Network for Screen Content Video Quality Enhancement
arXiv:2609.39894v1 Announce Type: new Abstract: Different from natural videos, Screen Content Videos (SCVs) are characterized by abrupt motion, scene switches, and high-frequency details such as text...
arXiv AI
4d ago
Rollout-Marginal Distillation for Long-Horizon Autoregressive Video Generation
arXiv:2609.37925v1 Announce Type: cross Abstract: Autoregressive (AR) video diffusion enables low-latency, streamable video generation, but prediction errors often accumulate over long rollouts. Trai...
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.
arXiv Computer Vision
Sep 3
TAME: Temporal-Aware Mixture-of-Experts for Text-Video Retrieval
TAME is a CLIP‑based framework for Text‑Video Retrieval that incorporates temporal modeling through three key innovations: sparse Mixture‑of‑Experts layers with frame‑consistent routing, Frame‑Temporal tokens that aggregate cross‑frame information, and a Cross‑Temporal Interaction and Aggregation module for refining frame‑wise similarities. These components enable the model to capture both local visual patterns and long‑range temporal dependencies, leading to consistent performance gains over CLIP‑based baselines on multiple TVR benchmarks, including a 4.0 R@1 improvement on MSR‑VTT. The code is publicly available on GitHub.
Hugging Face Trending Papers
Sep 12
SGWIB:Sliced Gromov-Wasserstein Information Bottleneck for Video Highlight Detection
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 AI
Jul 10
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
arXiv:2607. 07907v1 Announce Type: cross Abstract: With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data.