1 citations · 1 across the 8 of their papers we have counts for
8 papers
Delta Activations: A Representation for Finetuned Large Language Models
Zhiqiu Xu, Amish Sethi, Mayur Naik +1
The success of powerful open source Large Language Models (LLMs) has enabled the community to create a vast collection of post-trained models adapted to specific tasks and domains.…
Video Decomposition Prior: A Methodology to Decompose Videos into Layers
Gaurav Shrivastava, Ser-Nam Lim, Abhinav Shrivastava
In the evolving landscape of video enhancement and editing methodologies, a majority of deep learning techniques often rely on extensive datasets of observed input and ground truth…
Towards Chunk-Wise Generation for Long Videos
Siyang Zhang, Ser-Nam Lim
Generating long-duration videos has always been a significant challenge due to the inherent complexity of spatio-temporal domain and the substantial GPU memory demands required to…
Fast Encoding and Decoding for Implicit Video Representation
Hao Chen, Saining Xie, Ser-Nam Lim +1
Despite the abundant availability and content richness for video data, its high-dimensionality poses challenges for video research. Recent advancements have explored the implicit r…
Composing Object Relations and Attributes for Image-Text Matching
Khoi Pham, Chuong Huynh, Ser-Nam Lim +1
We study the visual semantic embedding problem for image-text matching. Most existing work utilizes a tailored cross-attention mechanism to perform local alignment across the two i…
UVIS: Unsupervised Video Instance Segmentation
Shuaiyi Huang, Saksham Suri, Kamal Gupta +3
Video instance segmentation requires classifying, segmenting, and tracking every object across video frames. Unlike existing approaches that rely on masks, boxes, or category label…
Object Recognition as Next Token Prediction
Kaiyu Yue, Bor-Chun Chen, Jonas Geiping +3
We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embed…
Riemannian Residual Neural Networks
Isay Katsman, Eric Ming Chen, Sidhanth Holalkere +4
Recent methods in geometric deep learning have introduced various neural networks to operate over data that lie on Riemannian manifolds. Such networks are often necessary to learn…