Search Results for author:
Found 7 papers, 5 papers with code
Predicting Long-horizon Futures by Conditioning on Geometry and Time
To address both challenges, our key insight is to leverage the large-scale pretraining of image diffusion models which can handle multi-modality.
TAO-Amodal: A Benchmark for Tracking Any Object Amodally
Amodal perception, the ability to comprehend complete object structures from partial visibility, is a fundamental skill, even for infants.
Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting
One promising self-supervised task is 3D point cloud forecasting from unannotated LiDAR sequences.
Differentiable Raycasting for Self-supervised Occupancy Forecasting
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.
BURST: A Benchmark for Unifying Object Recognition, Segmentation and Tracking in Video
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
Detecting Invisible People
We demonstrate that current detection and tracking systems perform dramatically worse on this task.
TAO: A Large-Scale Benchmark for Tracking Any Object
To this end, we ask annotators to label objects that move at any point in the video, and give names to them post factum.
