VFM4SDG is a dual-prior framework that distills cross-domain stable relations from VFMs into DETR encoders and injects semantic-contextual priors into decoder queries to reduce missed detections in single-domain generalized object detection.
Is imagenet worth 1 video? learning strong image encoders from 1 long unlabelled video
4 Pith papers cite this work. Polarity classification is still indexing.
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Text-only contrastive fine-tuning of an MLLM with hard negatives produces embeddings that handle temporal, negation, and multimodal nuances in video retrieval and achieves SOTA performance.
BabyCL learns word-referent mappings from egocentric video in a single chronological pass via streaming visual learning, dual replay, and three contrastive losses, outperforming streaming baselines on the SAYCam 4AFC benchmark.
DiLA uses content-structure disentanglement driven by predictive bottlenecks to create semantically structured latent actions for high-fidelity video world models.
citing papers explorer
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VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection
VFM4SDG is a dual-prior framework that distills cross-domain stable relations from VFMs into DETR encoders and injects semantic-contextual priors into decoder queries to reduce missed detections in single-domain generalized object detection.
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Adapting MLLMs for Nuanced Video Retrieval
Text-only contrastive fine-tuning of an MLLM with hard negatives produces embeddings that handle temporal, negation, and multimodal nuances in video retrieval and achieves SOTA performance.
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Continual Visual and Verbal Learning Through a Child's Egocentric Input
BabyCL learns word-referent mappings from egocentric video in a single chronological pass via streaming visual learning, dual replay, and three contrastive losses, outperforming streaming baselines on the SAYCam 4AFC benchmark.
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DiLA: Disentangled Latent Action World Models
DiLA uses content-structure disentanglement driven by predictive bottlenecks to create semantically structured latent actions for high-fidelity video world models.