54 citations · 57 across the 7 of their papers we have counts for
11 papers
Clink! Chop! Thud! -- Learning Object Sounds from Real-World Interactions
Mengyu Yang, Yiming Chen, Haozheng Pei +3
Can a model distinguish between the sound of a spoon hitting a hardwood floor versus a carpeted one? Everyday object interactions produce sounds unique to the objects involved. We…
LCA-on-the-Line: Benchmarking Out-of-Distribution Generalization with Class Taxonomies
Jia Shi, Gautam Gare, Jinjin Tian +6
We tackle the challenge of predicting models' Out-of-Distribution (OOD) performance using in-distribution (ID) measurements without requiring OOD data. Existing evaluations with "E…
Planning with Adaptive World Models for Autonomous Driving
Arun Balajee Vasudevan, Neehar Peri, Jeff Schneider +1
Motion planning is crucial for safe navigation in complex urban environments. Historically, motion planners (MPs) have been evaluated with procedurally-generated simulators like CA…
The Un-Kidnappable Robot: Acoustic Localization of Sneaking People
Mengyu Yang, Patrick Grady, Samarth Brahmbhatt +3
How easy is it to sneak up on a robot? We examine whether we can detect people using only the incidental sounds they produce as they move, even when they try to be quiet. We collec…
Sound and Visual Representation Learning with Multiple Pretraining Tasks
Arun Balajee Vasudevan, Dengxin Dai, Luc Van Gool
Different self-supervised tasks (SSL) reveal different features from the data. The learned feature representations can exhibit different performance for each downstream task. In th…
Binaural SoundNet: Predicting Semantics, Depth and Motion with Binaural Sounds
Dengxin Dai, Arun Balajee Vasudevan, Jiri Matas +1
Humans can robustly recognize and localize objects by using visual and/or auditory cues. While machines are able to do the same with visual data already, less work has been done wi…