EVIDENT routes MLLM adaptation for video temporal grounding through entity-grounded visual evidence using an Entity Bottleneck Adapter, Entity-Binding Distillation, and Entity-to-eVidence gating to improve cross-domain robustness.
Uncovering hidden challenges in query- based video moment retrieval
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
SpotSound adds a hallucination-suppressing objective and a needle-in-haystack benchmark to audio-language models, reaching state-of-the-art temporal grounding while keeping general task performance.
citing papers explorer
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EVIDENT: Routing MLLM Adaptation through Entity-Grounded Visual Evidence for Cross-Domain Video Temporal Grounding
EVIDENT routes MLLM adaptation for video temporal grounding through entity-grounded visual evidence using an Entity Bottleneck Adapter, Entity-Binding Distillation, and Entity-to-eVidence gating to improve cross-domain robustness.
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EvoGround: Self-Evolving Video Agents for Video Temporal Grounding
A proposer-solver agent pair achieves supervised-level video temporal grounding and fine-grained captioning from 2.5K unlabeled videos via self-reinforcing evolution.
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SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding
SpotSound adds a hallucination-suppressing objective and a needle-in-haystack benchmark to audio-language models, reaching state-of-the-art temporal grounding while keeping general task performance.