5 citations · 9 across the 8 of their papers we have counts for
6 papers · 1 filter
IntCoOp: Interpretability-Aware Vision-Language Prompt Tuning
Soumya Suvra Ghosal, Samyadeep Basu, Soheil Feizi +1
Image-text contrastive models such as CLIP learn transferable and robust representations for zero-shot transfer to a variety of downstream tasks. However, to obtain strong downstre…
Understanding Information Storage and Transfer in Multi-modal Large Language Models
Samyadeep Basu, Martin Grayson, Cecily Morrison +3
Understanding the mechanisms of information storage and transfer in Transformer-based models is important for driving model understanding progress. Recent work has studied these me…
Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models
Mazda Moayeri, Samyadeep Basu, Sriram Balasubramanian +4
Recent text-to-image generative models such as Stable Diffusion are extremely adept at mimicking and generating copyrighted content, raising concerns amongst artists that their uni…
Localizing and Editing Knowledge in Text-to-Image Generative Models
Samyadeep Basu, Nanxuan Zhao, Vlad Morariu +2
Text-to-Image Diffusion Models such as Stable-Diffusion and Imagen have achieved unprecedented quality of photorealism with state-of-the-art FID scores on MS-COCO and other generat…
EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods
Samyadeep Basu, Mehrdad Saberi, Shweta Bhardwaj +5
A plethora of text-guided image editing methods have recently been developed by leveraging the impressive capabilities of large-scale diffusion-based generative models such as Imag…
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning
Samyadeep Basu, Daniela Massiceti, Shell Xu Hu +1
Few-shot classification (FSC) entails learning novel classes given only a few examples per class after a pre-training (or meta-training) phase on a set of base classes. Recent work…