47 citations · 63 across the 21 of their papers we have counts for
16 papers · 1 filter
VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations
Maitreya Patel, Jingtao Li, Weiming Zhuang +2
We introduce an efficient, resolution-agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios, narrowing the gap to diffus…
MentalBlackboard: Evaluating Spatial Visualization via Mathematical Transformations
Nilay Yilmaz, Maitreya Patel, Naga Sai Abhiram Kusumba +2
Spatial visualization is the mental ability to imagine, transform, and manipulate the spatial characteristics of objects and actions. This intelligence is a part of human cognition…
Harnessing Synthetic Preference Data for Enhancing Temporal Understanding of Video-LLMs
Sameep Vani, Shreyas Jena, Maitreya Patel +3
While Video Large Language Models (Video-LLMs) have demonstrated remarkable performance across general video understanding benchmarks-particularly in video captioning and descripti…
AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models
Vatsal Malaviya, Agneet Chatterjee, Maitreya Patel +2
Text-to-Image (T2I) models have recently achieved remarkable success in generating images from textual descriptions. However, challenges still persist in accurately rendering compl…
RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions
Bimsara Pathiraja, Maitreya Patel, Shivam Singh +2
Despite recent advances in inversion and instruction-based image editing, existing approaches primarily excel at editing single, prominent objects but significantly struggle when a…
TextInVision: Text and Prompt Complexity Driven Visual Text Generation Benchmark
Forouzan Fallah, Maitreya Patel, Agneet Chatterjee +3
Generating images with embedded text is crucial for the automatic production of visual and multimodal documents, such as educational materials and advertisements. However, existing…
VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning
Nilay Yilmaz, Maitreya Patel, Yiran Lawrence Luo +4
Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information. Despite their exceptional performance on visual understanding b…
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…
-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…
ECLIPSE: A Resource-Efficient Text-to-Image Prior for Image Generations
Maitreya Patel, Changhoon Kim, Sheng Cheng +2
Text-to-image (T2I) diffusion models, notably the unCLIP models (e.g., DALL-E-2), achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks, at the cost o…
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models
Changhoon Kim, Kyle Min, Maitreya Patel +2
The rapid advancement of generative models, facilitating the creation of hyper-realistic images from textual descriptions, has concurrently escalated critical societal concerns suc…
ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models
Maitreya Patel, Tejas Gokhale, Chitta Baral +1
The ability to understand visual concepts and replicate and compose these concepts from images is a central goal for computer vision. Recent advances in text-to-image (T2I) models…
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering
Maitreya Patel, Tejas Gokhale, Chitta Baral +1
Videos often capture objects, their visible properties, their motion, and the interactions between different objects. Objects also have physical properties such as mass, which the…