7 papers · 1 filter
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…
Learning 3D Texture-Aware Representations for Parsing Diverse Human Clothing and Body Parts
Kiran Chhatre, Christopher Peters, Srikrishna Karanam
Existing methods for human parsing into body parts and clothing often use fixed mask categories with broad labels that obscure fine-grained clothing types. Recent open-vocabulary s…
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…
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models
Tripti Shukla, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of conditional text-to-image synthesis with diffusion models. Most recent works need to either finetune specific parts of the base diffusion model or introd…
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…