103 citations · 213 across the 6 of their papers we have counts for
6 papers
Hidden in plain sight: VLMs overlook their visual representations
Stephanie Fu, Tyler Bonnen, Devin Guillory +1
Language provides a natural interface to specify and evaluate performance on visual tasks. To realize this possibility, vision language models (VLMs) must successfully integrate vi…
Neural Network Diffusion
Kai Wang, Dongwen Tang, Boya Zeng +7
Diffusion models have achieved remarkable success in image and video generation. In this work, we demonstrate that diffusion models can also \textit{generate high-performing neural…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Colorado J. Reed, Ritwik Gupta, Shufan Li +7
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks wi…
Deep Mixture of Experts via Shallow Embedding
Xin Wang, Fisher Yu, Lisa Dunlap +5
Larger networks generally have greater representational power at the cost of increased computational complexity. Sparsifying such networks has been an active area of research but h…
SkipNet: Learning Dynamic Routing in Convolutional Networks
Xin Wang, Fisher Yu, Zi-Yi Dou +2
While deeper convolutional networks are needed to achieve maximum accuracy in visual perception tasks, for many inputs shallower networks are sufficient. We exploit this observatio…