465 citations · 1.7k across the 47 of their papers we have counts for
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cs.CV2023
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Hritik Bansal, Nishad Singhi, Yu Yang +3
Multimodal contrastive pretraining has been used to train multimodal representation models, such as CLIP, on large amounts of paired image-text data. However, previous studies have…
cs.CV2023★ 14 cited
Leaving Reality to Imagination: Robust Classification via Generated Datasets
Hritik Bansal, Aditya Grover
Recent research on robustness has revealed significant performance gaps between neural image classifiers trained on datasets that are similar to the test set, and those that are fr…
cs.CV2022★ 46 cited
CyCLIP: Cyclic Contrastive Language-Image Pretraining
Shashank Goel, Hritik Bansal, Sumit Bhatia +3
Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classific…