170 citations · 173 across the 2 of their papers we have counts for
6 papers
Detecting Invisible People
Tarasha Khurana, Achal Dave, Deva Ramanan
Monocular object detection and tracking have improved drastically in recent years, but rely on a key assumption: that objects are visible to the camera. Many offline tracking appro…
Measuring Robustness to Natural Distribution Shifts in Image Classification
Rohan Taori, Achal Dave, Vaishaal Shankar +3
We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets. Most research on robustness focuses on synthetic image perturbat…
TAO: A Large-Scale Benchmark for Tracking Any Object
Achal Dave, Tarasha Khurana, Pavel Tokmakov +2
For many years, multi-object tracking benchmarks have focused on a handful of categories. Motivated primarily by surveillance and self-driving applications, these datasets provide…
Learning to Track Any Object
Achal Dave, Pavel Tokmakov, Cordelia Schmid +1
Object tracking can be formulated as "finding the right object in a video". We observe that recent approaches for class-agnostic tracking tend to focus on the "finding" part, but l…
Do Image Classifiers Generalize Across Time?
Vaishaal Shankar, Achal Dave, Rebecca Roelofs +3
We study the robustness of image classifiers to temporal perturbations derived from videos. As part of this study, we construct two datasets, ImageNet-Vid-Robust and YTBB-Robust ,…
Towards Segmenting Anything That Moves
Achal Dave, Pavel Tokmakov, Deva Ramanan
Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has…