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20122022
most citedPedestrian Detection with Unsupervised Multi-Stage Feature Learning

29 citations · 72 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2021

With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +2

Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat di…

cs.CV20203 cited

Counting Out Time: Class Agnostic Video Repetition Counting in the Wild

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +2

We present an approach for estimating the period with which an action is repeated in a video. The crux of the approach lies in constraining the period prediction module to use temp…

cs.CV201913 cited

Online Object Representations with Contrastive Learning

Sören Pirk, Mohi Khansari, Yunfei Bai +2

We propose a self-supervised approach for learning representations of objects from monocular videos and demonstrate it is particularly useful in situated settings such as robotics.…

cs.CV2019

Temporal Cycle-Consistency Learning

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson +2

We introduce a self-supervised representation learning method based on the task of temporal alignment between videos. The method trains a network using temporal cycle consistency (…

cs.CV2018

Learning Actionable Representations from Visual Observations

Debidatta Dwibedi, Jonathan Tompson, Corey Lynch +1

In this work we explore a new approach for robots to teach themselves about the world simply by observing it. In particular we investigate the effectiveness of learning task-agnost…

cs.CV201229 cited

Pedestrian Detection with Unsupervised Multi-Stage Feature Learning

Pierre Sermanet, Koray Kavukcuoglu, Soumith Chintala +1

Pedestrian detection is a problem of considerable practical interest. Adding to the list of successful applications of deep learning methods to vision, we report state-of-the-art a…