IAE-VTG: Interaction-Aligned Action-Entity Video Temporal Grounding
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.
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.
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.
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.
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.
arXiv:2611.06302v1 Announce Type: new Abstract: Fine-grained action understanding requires vision-language models to ground what an agent does, how it acts, and why it acts in spatio-temporal evidenc...
arXiv:2610.08674v1 Announce Type: new Abstract: Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independentl...