8 citations · 10 across the 5 of their papers we have counts for
7 papers
Coarse-To-Fine Tensor Trains for Compact Visual Representations
Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen +3
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstructio…
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen +7
Recent advances in machine learning have shown that Reinforcement Learning from Human Feedback (RLHF) can improve machine learning models and align them with human preferences. Alt…
Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation
Guy Yariv, Itai Gat, Sagie Benaim +3
We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to b…
Polynomial Neural Fields for Subband Decomposition and Manipulation
Guandao Yang, Sagie Benaim, Varun Jampani +5
Neural fields have emerged as a new paradigm for representing signals, thanks to their ability to do it compactly while being easy to optimize. In most applications, however, neura…
FewGAN: Generating from the Joint Distribution of a Few Images
Lior Ben-Moshe, Sagie Benaim, Lior Wolf
We introduce FewGAN, a generative model for generating novel, high-quality and diverse images whose patch distribution lies in the joint patch distribution of a small number of N>1…
Text-Driven Stylization of Video Objects
Sebastian Loeschcke, Serge Belongie, Sagie Benaim
We tackle the task of stylizing video objects in an intuitive and semantic manner following a user-specified text prompt. This is a challenging task as the resulting video must sat…