16 citations · 49 across the 20 of their papers we have counts for
6 papers · 1 filter
AgentComp: From Agentic Reasoning to Compositional Mastery in Text-to-Image Models
Arman Zarei, Jiacheng Pan, Matthew Gwilliam +2
Text-to-image generative models have achieved remarkable visual quality but still struggle with compositionalityaccurately capturing object relationships, attribute bindings, an…
Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor
Vatsal Agarwal, Matthew Gwilliam, Gefen Kohavi +3
Recent advances in multimodal large language models (MLLMs) have enabled image-based question-answering capabilities. However, a key limitation is the use of CLIP as the visual enc…
How to Design and Train Your Implicit Neural Representation for Video Compression
Matthew Gwilliam, Roy Zhang, Namitha Padmanabhan +2
Implicit neural representation (INR) methods for video compression have recently achieved visual quality and compression ratios that are competitive with traditional pipelines. How…
Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion Model
Anirud Aggarwal, Abhinav Shrivastava, Matthew Gwilliam
Diffusion-based image generation models excel at producing high-quality synthetic content, but suffer from slow and computationally expensive inference. Prior work has attempted to…
Utilization of Neighbor Information for Image Classification with Different Levels of Supervision
Gihan Jayatilaka, Abhinav Shrivastava, Matthew Gwilliam
We propose to bridge the gap between semi-supervised and unsupervised image recognition with a flexible method that performs well for both generalized category discovery (GCD) and…
Accelerate High-Quality Diffusion Models with Inner Loop Feedback
Matthew Gwilliam, Han Cai, Di Wu +2
We propose Inner Loop Feedback (ILF), a novel approach to accelerate diffusion models' inference. ILF trains a lightweight module to predict future features in the denoising proces…