Aug 2026· The Visual Computer· Vol 42· 0 citations· 29 references
TL;DR
DiaVTG, a novel VTG framework designed to enhance temporal localization precision, is proposed to reformulate temporal localization as a video understanding problem and demonstrates that the training-free method consistently improves performance across various Vid-LLM architectures.
The proposed Temporal Tree of Thought T^3, a training-free framework for adaptive coarse-to-fine long-video understanding, constructs a question-agnostic hierarchical temporal tree via recursive temporally constrained clustering, where each node represents a contiguous segment with an informative key frame.
Structured video prompting is introduced, a training-free inference-time method that augments the input video with lightweight spatial structure and temporal structure, providing explicit anchors for organizing evidence across space and time without changing model weights or decoding.
Spatio-temporal video grounding (STVG) requires models to identify when a referred event occurs and localize the target entity throughout that interval. Existing multimodal large language models typically serialize dense localization trajectories autoregressively, causing decoding latency to grow with tube length and a...
H. Rasheed, H. Siddiqui, Ming-Hsuan Yang et al.· 0 citations
This work proposes REcognition-based Zero-shot Extraction (REZE), a simple training-free method that splits the video into short clips, asks the model for a clip-level confidence score for the query, and uses a deterministic algorithm to convert the resulting score curve into the output required by the task.
Boyang Albert Li, Chen-Hui Gou, Jianfei Cai· 0 citations
A cross-modal temporal alignment framework that combines a multi-scale temporal convolutional encoder with capsule-based dynamic routing, jointly optimizing temporal boundary prediction, cross-modal semantic alignment, and capsule diversity is proposed.
Gengtian Shi, Chen-Hao Wu, Shao-Fei Wang et al.· IEEE Access· 0 citations
VideoRouter (VR) is proposed that rethinks long-video understanding as coordinating complementary evidence views rather than selecting a single subset of frames, and introduces a verification-guided router to determine which view is better supported by the selected evidence and select the final answer.
Ziling Huang, Yuki M. Asano, Shin'ichi Satoh· 0 citations
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