arXiv Computer Vision
Aug 21

ID-VTG: Image-Disambiguated Video Temporal Grounding

arXiv:2608. 20127v1 Announce Type: new Abstract: Video Temporal Grounding (VTG) faces significant challenges when natural language queries must distinguish between multiple events involving visually similar entities, particularly when relying on fine-grained visual attributes that are difficult to describe accurately in words alone.

By Minghang Zheng, Jingli Wei, Hongyi Yang, Yang Liu
arXiv AI
Oct 31

Invert4TVG: A Temporal Video Grounding Framework with Inversion Tasks Preserving Action Understanding Ability

The paper introduces Invert4TVG, a temporal video grounding framework that adds inversion tasks to preserve action understanding. It defines three inversion tasks—Verb Completion, Action Recognition, and Video Description—derived from existing TVG annotations and integrates them via reinforcement learning. Experiments demonstrate a 7.1% improvement in R1@0.7 on Charades-STA for a 3B model, outperforming state‑of‑the‑art methods.

By Zhaoyu Chen, Hongnan Lin, Yongwei Nie, Fei Ma, Xuemiao Xu, Fei Yu, Chengjiang Long
arXiv Computer Vision
6d ago

TraCE: Trajectory-Aware Concepts for Explainable Video Understanding

TraCE introduces a trajectory‑aware concept framework for explainable video understanding, clustering optical‑flow trajectories to discover motion concepts that capture dynamic evidence from people, objects, and interactions. These concepts, along with static object and scene concepts, form a typed interface that supports action recognition and question‑conditioned video question answering. Across six benchmarks, TraCE achieves competitive performance with only a 1.0‑point drop in action recognition accuracy and a 0.4‑point gain in VQA, while motion perturbation and concept‑removal tests demonstrate semantic alignment and decision faithfulness.

By Wooil Lee, Junpyo Hong, Jongseo Lee, Tuomas Oikarinen, Tsui-Wei Weng, Jinwoo Choi
arXiv AI
Nov 5

Grounding in the Dark: Text-Only Training for Zero-Shot Video Temporal Localization

The paper explores training a large language model solely on textual video descriptions to perform zero‑shot video temporal localization, eliminating the need for paired video‑text data during training. Using a 134K‑example corpus and adapting Qwen3‑8B with LoRA on CLIP or LLM2CLIP embeddings, the authors evaluate on Charades‑STA and QV‑Highlights, achieving modest performance when directly replacing text with frame embeddings. They introduce Nearest Entity Replacement (NER), a training‑free mapping that represents each video frame by the mean of its nearest text‑entity embeddings, which significantly improves results and narrows the modality gap.

By Divyanshu Mishra, Sabine Sternig, Rakshith Shetty, Erhan Gundogdu