16 citations · 49 across the 20 of their papers we have counts for
22 papers
TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos
Namitha Padmanabhan, Matthew Gwilliam, Abhinav Shrivastava
Implicit Neural Representations (INRs) have recently demonstrated impressive performance for video compression. However, since a separate INR must be overfit for each video, scalin…
Towards Understanding Best Practices for Quantization of Vision-Language Models
Gautom Das, Vincent La, Ethan Lau +2
Large language models (LLMs) deliver impressive results for a variety of tasks, but state-of-the-art systems require fast GPUs with large amounts of memory. To reduce both the memo…
Implicit Neural Representation Facilitates Unified Universal Vision Encoding
Matthew Gwilliam, Xiao Wang, Xuefeng Hu +1
Models for image representation learning are typically designed for either recognition or generation. Various forms of contrastive learning help models learn to convert images to e…
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…