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.
arXiv preprint arXiv:2408.11208 , year=
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VLMs exhibit size, center, and saliency biases in scene understanding, relying less on people than humans do, with size bias as a key driver of divergence.
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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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Revealing the Gap in Human and VLM Scene Perception through Counterfactual Semantic Saliency
VLMs exhibit size, center, and saliency biases in scene understanding, relying less on people than humans do, with size bias as a key driver of divergence.