TransVLM formalizes Shot Transition Detection as identifying full temporal transition segments rather than single cut points and introduces a VLM that injects optical flow as a motion prior via simple feature fusion, plus a synthetic data engine and benchmark.
Clip4clip: An empirical study of clip for end to end video clip retrieval and captioning.Neurocomputing, 508:293–304, 2022
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InstrAction pretrains video foundation models using action-centric data filtering, hard negatives, an Action Perceiver module, DTW-Align, and Masked Action Modeling to reduce static bias and outperform prior models on a new InstrAct Bench for semantic, procedural, and retrieval tasks.
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.
citing papers explorer
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TransVLM: A Vision-Language Framework and Benchmark for Detecting Any Shot Transitions
TransVLM formalizes Shot Transition Detection as identifying full temporal transition segments rather than single cut points and introduces a VLM that injects optical flow as a motion prior via simple feature fusion, plus a synthetic data engine and benchmark.
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InstrAct: Towards Action-Centric Understanding in Instructional Videos
InstrAction pretrains video foundation models using action-centric data filtering, hard negatives, an Action Perceiver module, DTW-Align, and Masked Action Modeling to reduce static bias and outperform prior models on a new InstrAct Bench for semantic, procedural, and retrieval tasks.
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Calibrated Multimodal Representation Learning with Missing Modalities
CalMRL mitigates anchor shift in multimodal representation learning by calibrating incomplete alignments through representation-level imputation of missing modalities using priors and a bi-step optimization with closed-form shared latent posteriors.