16 citations · 49 across the 20 of their papers we have counts for
3 papers · 1 filter
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…