24 citations · 39 across the 17 of their papers we have counts for
4 papers · 1 filter
Pinpoint Counterfactuals: Reducing social bias in foundation models via localized counterfactual generation
Kirill Sirotkin, Marcos Escudero-Viñolo, Pablo Carballeira +3
Foundation models trained on web-scraped datasets propagate societal biases to downstream tasks. While counterfactual generation enables bias analysis, existing methods introduce a…
Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +3
Recent contrastive multimodal vision-language models like CLIP have demonstrated robust open-world semantic understanding, becoming the standard image backbones for vision-language…
Test-Time Adaptation with SaLIP: A Cascade of SAM and CLIP for Zero shot Medical Image Segmentation
Sidra Aleem, Fangyijie Wang, Mayug Maniparambil +6
The Segment Anything Model (SAM) and CLIP are remarkable vision foundation models (VFMs). SAM, a prompt driven segmentation model, excels in segmentation tasks across diverse domai…
Do Vision and Language Encoders Represent the World Similarly?
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +4
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their…