5 papers
ARM: An AutoRegressive Large Multimodal Model with Unified Discrete Representations
Junke Wang, Xiao Wang, Jiacheng Pan +16
This paper introduces ARM, a discrete representation-based AutoRegressive Model that unifies image understanding, generation, and editing within a next-token prediction framework.…
Going Down Memory Lane: Scaling Tokens for Video Stream Understanding with Dynamic KV-Cache Memory
Vatsal Agarwal, Saksham Suri, Matthew Gwilliam +2
Streaming video understanding requires models to robustly encode, store, and retrieve information from a continuous video stream to support accurate video question answering (VQA).…
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…
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…