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TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
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TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding
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Video Temporal Grounding (VTG) aims to localize specific video segments corresponding to natural language queries. While recent Large Vision-Language Models (LVLMs) employ Reinforcement Learning to generate Chains-of-Thought (CoT), they typically rely solely on outcome-based supervision. Consequently, this often leads to hallucinations, where the reasoning process becomes disconnected from the visual content and the final prediction. Existing attempts to mitigate this by relying on external supervision from larger models or separate reward models are computationally expensive and prone to rigid patterns. To address these challenges, we propose TAR (Temporal Anchor-Constrained Reasoning), a framework that introduces the temporal anchor (T-anchor) as a transparent and auditable checkpoint mechanism. T-anchor enforces progressive refinement within the CoT, compelling the model to continuously ground its intermediate thoughts in visual evidence and iteratively calibrate temporal predictions, thereby significantly enhancing the faithfulness and autonomy of the reasoning process and final accuracy. Furthermore, we introduce a bootstrapping paradigm that automatically harvests high-quality CoT data using only a standard 7B model, eliminating the dependency on ultra-large models. Extensive experiments demonstrate that TAR achieves state-of-the-art performance and generates faithful, autonomous, and progressively refined reasoning traces.
Forward citations
Cited by 5 Pith papers
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MarkIt: Training-Free Visual Markers for Precise Video Temporal Grounding
MarkIt uses a query-to-mask bridge with open-vocabulary segmentation to add visual markers and frame indices to videos, enabling Vid-LLMs to achieve state-of-the-art temporal grounding on moment retrieval and highligh...
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TimePLE: Rethinking Temporal Representation for Video Temporal Grounding
TimePLE predicts a whole video interval as a joint distribution over a position-duration square, rather than predicting start and end separately, and reports higher mIoU across four VTG benchmarks.
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MarkIt: Training-Free Visual Markers for Precise Video Temporal Grounding
MarkIt converts videos into query-conditioned marked versions via a linguistic-parsing and open-vocabulary segmentation bridge that embeds instance masks, semantic markers, and frame indices to improve Vid-LLM tempora...
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TempR1: Improving Temporal Understanding of MLLMs via Temporal-Aware Multi-Task Reinforcement Learning
TempR1 applies temporal-aware multi-task RL using GRPO and three types of localization rewards to achieve SOTA temporal understanding in MLLMs with synergistic gains from joint optimization.
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Watch, Remember, Reason: Human-View Video Understanding with MLLMs
This is a survey that frames video MLLM research via a human-view formulation of perceptual representations, memory states, reasoning traces, and predictions, then reviews methods, datasets, benchmarks, and open problems.
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