8 citations · 17 across the 16 of their papers we have counts for
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cs.LG2024★ 1 cited
Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
Chen Huang, Skyler Seto, Samira Abnar +3
Large pretrained vision-language models like CLIP have shown promising generalization capability, but may struggle in specialized domains (e.g., satellite imagery) or fine-grained…
cs.LG2024★ 2 cited
How JEPA Avoids Noisy Features: The Implicit Bias of Deep Linear Self Distillation Networks
Etai Littwin, Omid Saremi, Madhu Advani +4
Two competing paradigms exist for self-supervised learning of data representations. Joint Embedding Predictive Architecture (JEPA) is a class of architectures in which semantically…
cs.CV2024★ 1 cited
Overcoming the Pitfalls of Vision-Language Model Finetuning for OOD Generalization
Yuhang Zang, Hanlin Goh, Josh Susskind +1
Existing vision-language models exhibit strong generalization on a variety of visual domains and tasks. However, such models mainly perform zero-shot recognition in a closed-set ma…