47 citations · 63 across the 21 of their papers we have counts for
6 papers · 1 filter
Steering Rectified Flow Models in the Vector Field for Controlled Image Generation
Maitreya Patel, Song Wen, Dimitris N. Metaxas +1
Diffusion models (DMs) excel in photorealism, image editing, and solving inverse problems, aided by classifier-free guidance and image inversion techniques. However, rectified flow…
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model
Sheng Cheng, Maitreya Patel, Yezhou Yang
Despite advancements in text-to-image models, generating images that precisely align with textual descriptions remains challenging due to misalignment in training data. In this pap…
TripletCLIP: Improving Compositional Reasoning of CLIP via Synthetic Vision-Language Negatives
Maitreya Patel, Abhiram Kusumba, Sheng Cheng +4
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the train…
Help Me Identify: Is an LLM+VQA System All We Need to Identify Visual Concepts?
Shailaja Keyur Sampat, Maitreya Patel, Yezhou Yang +1
An ability to learn about new objects from a small amount of visual data and produce convincing linguistic justification about the presence/absence of certain concepts (that collec…
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs
Yuval Reif, Roy Schwartz
Large language models (LLMs) have shown remarkable adaptability to diverse tasks, by leveraging context prompts containing instructions, or minimal input-output examples. However,…
-ECLIPSE: Multi-Concept Personalized Text-to-Image Diffusion Models by Leveraging CLIP Latent Space
Maitreya Patel, Sangmin Jung, Chitta Baral +1
Despite the recent advances in personalized text-to-image (P-T2I) generative models, it remains challenging to perform finetuning-free multi-subject-driven T2I in a resource-effici…