22 citations · 26 across the 12 of their papers we have counts for
11 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…
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…
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…
Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of independently, in a disentangled fashion, controlling the outputs of text-to-image diffusion models with color and style attributes of a user-supplied re…
AlignIT: Enhancing Prompt Alignment in Customization of Text-to-Image Models
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
We consider the problem of customizing text-to-image diffusion models with user-supplied reference images. Given new prompts, the existing methods can capture the key concept from…
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…
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, Tripti Shukla +1
We consider the problem of constraining diffusion model outputs with a user-supplied reference image. Our key objective is to extract multiple attributes (e.g., color, object, layo…
Iterative Multi-granular Image Editing using Diffusion Models
K J Joseph, Prateksha Udhayanan, Tripti Shukla +4
Recent advances in text-guided image synthesis has dramatically changed how creative professionals generate artistic and aesthetically pleasing visual assets. To fully support such…
Learning with Difference Attention for Visually Grounded Self-supervised Representations
Aishwarya Agarwal, Srikrishna Karanam, Balaji Vasan Srinivasan
Recent works in self-supervised learning have shown impressive results on single-object images, but they struggle to perform well on complex multi-object images as evidenced by the…
A-STAR: Test-time Attention Segregation and Retention for Text-to-image Synthesis
Aishwarya Agarwal, Srikrishna Karanam, K J Joseph +3
While recent developments in text-to-image generative models have led to a suite of high-performing methods capable of producing creative imagery from free-form text, there are sev…