5 papers
LiteEmbed: Adapting CLIP to Rare Classes
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
Large-scale vision-language models such as CLIP achieve strong zero-shot recognition but struggle with classes that are rarely seen during pretraining, including newly emerging ent…
Concept Regions Matter: Benchmarking CLIP with a New Cluster-Importance Approach
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
Contrastive vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition yet remain vulnerable to spurious correlations, particularly background over-reliance. W…
Composing Parts for Expressive Object Generation
Harsh Rangwani, Aishwarya Agarwal, Kuldeep Kulkarni +2
Image composition and generation are processes where the artists need control over various parts of the generated images. However, the current state-of-the-art generation models, l…
TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction
Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
We consider the problem of single-source domain generalization. Existing methods typically rely on extensive augmentations to synthetically cover diverse domains during training. H…
CoCoNO: Attention Contrast-and-Complete for Initial Noise Optimization in Text-to-Image Synthesis
Aravindan Sundaram, Ujjayan Pal, Abhimanyu Chauhan +2
Despite recent advancements in text-to-image models, achieving semantically accurate images in text-to-image diffusion models is a persistent challenge. While existing initial late…