2.3k citations · 6.3k across the 61 of their papers we have counts for
10 papers · 1 filter
Benchmarking Robustness and Generalization in Multi-Agent Systems: A Case Study on Neural MMO
Yangkun Chen, Joseph Suarez, Junjie Zhang +18
We present the results of the second Neural MMO challenge, hosted at IJCAI 2022, which received 1600+ submissions. This competition targets robustness and generalization in multi-a…
Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
William Shen, Ge Yang, Alan Yu +3
Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed under…
MultiEarth 2023 -- Multimodal Learning for Earth and Environment Workshop and Challenge
Miriam Cha, Gregory Angelides, Mark Hamilton +6
The Multimodal Learning for Earth and Environment Workshop (MultiEarth 2023) is the second annual CVPR workshop aimed at the monitoring and analysis of the health of Earth ecosyste…
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Stephanie Fu, Netanel Tamir, Shobhita Sundaram +4
Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture…
StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners
Yonglong Tian, Lijie Fan, Phillip Isola +2
We investigate the potential of learning visual representations using synthetic images generated by text-to-image models. This is a natural question in the light of the excellent p…
Straightening Out the Straight-Through Estimator: Overcoming Optimization Challenges in Vector Quantized Networks
Minyoung Huh, Brian Cheung, Pulkit Agrawal +1
This work examines the challenges of training neural networks using vector quantization using straight-through estimation. We find that a primary cause of training instability is t…
Improving CLIP Training with Language Rewrites
Lijie Fan, Dilip Krishnan, Phillip Isola +2
Contrastive Language-Image Pre-training (CLIP) stands as one of the most effective and scalable methods for training transferable vision models using paired image and text data. CL…
Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
Tongzhou Wang, Antonio Torralba, Phillip Isola +1
In goal-reaching reinforcement learning (RL), the optimal value function has a particular geometry, called quasimetric structure. This paper introduces Quasimetric Reinforcement Le…
Persistent Nature: A Generative Model of Unbounded 3D Worlds
Lucy Chai, Richard Tucker, Zhengqi Li +2
Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of…
Steerable Equivariant Representation Learning
Sangnie Bhardwaj, Willie McClinton, Tongzhou Wang +4
Pre-trained deep image representations are useful for post-training tasks such as classification through transfer learning, image retrieval, and object detection. Data augmentation…