most citedFuture Frame Prediction Using Convolutional VRNN for Anomaly Detection

11 citations · 22 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2020

AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting

Mahesh Kumar Krishna Reddy, Mrigank Rochan, Yiwei Lu +1

We address the problem of image-based crowd counting. In particular, we propose a new problem called unlabeled scene-adaptive crowd counting. Given a new target scene, we would lik…

cs.CV20203 cited

Sentence Guided Temporal Modulation for Dynamic Video Thumbnail Generation

Mrigank Rochan, Mahesh Kumar Krishna Reddy, Yang Wang

We consider the problem of sentence specified dynamic video thumbnail generation. Given an input video and a user query sentence, the goal is to generate a video thumbnail that not…

cs.CV20204 cited

Adaptive Video Highlight Detection by Learning from User History

Mrigank Rochan, Mahesh Kumar Krishna Reddy, Linwei Ye +1

Recently, there is an increasing interest in highlight detection research where the goal is to create a short duration video from a longer video by extracting its interesting momen…

cs.CV20204 cited

Few-shot Scene-adaptive Anomaly Detection

Yiwei Lu, Frank Yu, Mahesh Kumar Krishna Reddy +1

We address the problem of anomaly detection in videos. The goal is to identify unusual behaviours automatically by learning exclusively from normal videos. Most existing approaches…

cs.CV2020

Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning

Mahesh Kumar Krishna Reddy, Mohammad Hossain, Mrigank Rochan +1

We consider the problem of few-shot scene adaptive crowd counting. Given a target camera scene, our goal is to adapt a model to this specific scene with only a few labeled images o…

cs.CV201911 cited

Future Frame Prediction Using Convolutional VRNN for Anomaly Detection

Yiwei Lu, Mahesh Kumar Krishna Reddy, Seyed shahabeddin Nabavi +1

Anomaly detection in videos aims at reporting anything that does not conform the normal behaviour or distribution. However, due to the sparsity of abnormal video clips in real life…