62 citations · 176 across the 28 of their papers we have counts for
3 papers · 1 filter
Spotting Temporally Precise, Fine-Grained Events in Video
James Hong, Haotian Zhang, Michaël Gharbi +2
We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason g…
Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning
Mayee F. Chen, Daniel Y. Fu, Avanika Narayan +4
An ideal learned representation should display transferability and robustness. Supervised contrastive learning (SupCon) is a promising method for training accurate models, but prod…
Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision
Mayee F. Chen, Daniel Y. Fu, Dyah Adila +4
Foundation models offer an exciting new paradigm for constructing models with out-of-the-box embeddings and a few labeled examples. However, it is not clear how to best apply found…