Search Results for author: Achal Dave

Found 12 papers, 5 papers with code

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

1 code implementation3 May 2022 Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt

Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts.

Opening up Open-World Tracking

no code implementations22 Apr 2021 Yang Liu, Idil Esen Zulfikar, Jonathon Luiten, Achal Dave, Deva Ramanan, Bastian Leibe, Aljoša Ošep, Laura Leal-Taixé

We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.

Multi-Object Tracking

Detecting Invisible People

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.

Monocular Depth Estimation Object Detection

Learning to Track Any Object

no code implementations25 Oct 2019 Achal Dave, Pavel Tokmakov, Cordelia Schmid, Deva Ramanan

Moreover, at test time the same network can be applied to detection and tracking, resulting in a unified approach for the two tasks.

Frame Instance Segmentation +5

Do Image Classifiers Generalize Across Time?

1 code implementation ICCV 2021 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

14 General Classification +1

A Systematic Framework for Natural Perturbations from Videos

no code implementations ICML Workshop Deep_Phenomen 2019 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.

14 Video Object Detection

Towards Segmenting Anything That Moves

no code implementations11 Feb 2019 Achal Dave, Pavel Tokmakov, Deva Ramanan

To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.

Action Detection Instance Segmentation +6

Predictive-Corrective Networks for Action Detection

no code implementations CVPR 2017 Achal Dave, Olga Russakovsky, Deva Ramanan

While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing.

Action Detection Optical Flow Estimation +1

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