activity
20162021
most citedVideoLSTM Convolves, Attends and Flows for Action Recognition

64 citations · 71 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Motion-Augmented Self-Training for Video Recognition at Smaller Scale

Kirill Gavrilyuk, Mihir Jain, Ilia Karmanov +1

The goal of this paper is to self-train a 3D convolutional neural network on an unlabeled video collection for deployment on small-scale video collections. As smaller video dataset…

cs.CV20202 cited

TimeGate: Conditional Gating of Segments in Long-range Activities

Noureldien Hussein, Mihir Jain, Babak Ehteshami Bejnordi

When recognizing a long-range activity, exploring the entire video is exhaustive and computationally expensive, as it can span up to a few minutes. Thus, it is of great importance…

cs.AI20184 cited

Multi-Fidelity Recursive Behavior Prediction

Mihir Jain, Kyle Brown, Ahmed K. Sadek

Predicting the behavior of surrounding vehicles is a critical problem in automated driving. We present a novel game theoretic behavior prediction model that achieves state of the a…

cs.CV2018

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

Victor Escorcia, Cuong D. Dao, Mihir Jain +2

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes…

cs.CV20161 cited

Tubelets: Unsupervised action proposals from spatiotemporal super-voxels

Mihir Jain, Jan van Gemert, Hervé Jégou +2

This paper considers the problem of localizing actions in videos as a sequences of bounding boxes. The objective is to generate action proposals that are likely to include the acti…

cs.CV201664 cited

VideoLSTM Convolves, Attends and Flows for Action Recognition

Zhenyang Li, Efstratios Gavves, Mihir Jain +1

We present a new architecture for end-to-end sequence learning of actions in video, we call VideoLSTM. Rather than adapting the video to the peculiarities of established recurrent…