no code implementations • 17 Apr 2024 • Tarasha Khurana, Deva Ramanan
To address both challenges, our key insight is to leverage the large-scale pretraining of image diffusion models which can handle multi-modality.
1 code implementation • 19 Dec 2023 • Cheng-Yen Hsieh, Kaihua Chen, Achal Dave, Tarasha Khurana, Deva Ramanan
Amodal perception, the ability to comprehend complete object structures from partial visibility, is a fundamental skill, even for infants.
1 code implementation • CVPR 2023 • Tarasha Khurana, Peiyun Hu, David Held, Deva Ramanan
One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences.
1 code implementation • 4 Oct 2022 • Tarasha Khurana, Peiyun Hu, Achal Dave, Jason Ziglar, David Held, Deva Ramanan
Self-supervised representations proposed for large-scale planning, such as ego-centric freespace, confound these two motions, making the representation difficult to use for downstream motion planners.
1 code implementation • 25 Sep 2022 • Ali Athar, Jonathon Luiten, Paul Voigtlaender, Tarasha Khurana, Achal Dave, Bastian Leibe, Deva Ramanan
Multiple existing benchmarks involve tracking and segmenting objects in video e. g., Video Object Segmentation (VOS) and Multi-Object Tracking and Segmentation (MOTS), but there is little interaction between them due to the use of disparate benchmark datasets and metrics (e. g. J&F, mAP, sMOTSA).
Ranked #4 on Long-tail Video Object Segmentation on BURST-val (using extra training data)
Long-tail Video Object Segmentation Multi-Object Tracking +6
1 code implementation • ICCV 2021 • Tarasha Khurana, Achal Dave, Deva Ramanan
We demonstrate that current detection and tracking systems perform dramatically worse on this task.
no code implementations • ECCV 2020 • Achal Dave, Tarasha Khurana, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan
To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum.