snehashismajhi
R&D Engineer || INRIA Sophia Antipolis France || Spatio-Temporal Activity Recognition Systems (STARS) Team
INRIA Sophia Antipolis FranceValbonne
Pinned Repositories
DAM-Anomaly-Detection
[AVSS21 Oral] A framework consisting of Dissimilarity Attention Module (DAM) to discriminate the anomaly instances from normal ones both at feature level and score level. In order to decide instances to be normal or anomaly, DAM takes local spatio-temporal (i.e. clips within a video) dissimilarities into account rather than the global temporal context of a video.
JointDetectClassify
[FG 2021 Poster] A network that jointly handles the anomaly detection and classification in a single framework by adopting a weakly-supervised learning paradigm. In weakly-supervised learning instead of dense temporal annotations, only video-level labels are sufficient for learning.
Temporal-Pooling-in-Inflated-3DCNN-for-Weakly-supervised-Video-Anomaly-Detection
Anomaly detection in surveillance videos requires significant attention in feature engineering to discriminate anomaly activity patterns from normal patterns. Keeping this in mind, this paper aims to extract superior quality spatio temporal features from Inflated 3DCNN followed by a temporal pooling strategy to intensify relevant spatio temporal feature in untrimmed anomalous videos. A superior temporal pooling strategy leads to better understanding of temporal dependency through LSTM model, which has become a necessary step for anomaly detection in surveillance videos. Thus, we propose a method consisting of an ideal temporal pooling strategy in inflated 3DCNN feature map along with LSTM model for temporal dependency encoding for weakly-supervised anomaly detection task. Our method is validated on a large scale video anomaly detection dataset, namely UCF-crime, resulting competitive performance in anomaly detection task with recent state-of-the-art methodologies.
TokenLearner
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"
snehashismajhi's Repositories
snehashismajhi/DAM-Anomaly-Detection
[AVSS21 Oral] A framework consisting of Dissimilarity Attention Module (DAM) to discriminate the anomaly instances from normal ones both at feature level and score level. In order to decide instances to be normal or anomaly, DAM takes local spatio-temporal (i.e. clips within a video) dissimilarities into account rather than the global temporal context of a video.
snehashismajhi/Temporal-Pooling-in-Inflated-3DCNN-for-Weakly-supervised-Video-Anomaly-Detection
Anomaly detection in surveillance videos requires significant attention in feature engineering to discriminate anomaly activity patterns from normal patterns. Keeping this in mind, this paper aims to extract superior quality spatio temporal features from Inflated 3DCNN followed by a temporal pooling strategy to intensify relevant spatio temporal feature in untrimmed anomalous videos. A superior temporal pooling strategy leads to better understanding of temporal dependency through LSTM model, which has become a necessary step for anomaly detection in surveillance videos. Thus, we propose a method consisting of an ideal temporal pooling strategy in inflated 3DCNN feature map along with LSTM model for temporal dependency encoding for weakly-supervised anomaly detection task. Our method is validated on a large scale video anomaly detection dataset, namely UCF-crime, resulting competitive performance in anomaly detection task with recent state-of-the-art methodologies.
snehashismajhi/JointDetectClassify
[FG 2021 Poster] A network that jointly handles the anomaly detection and classification in a single framework by adopting a weakly-supervised learning paradigm. In weakly-supervised learning instead of dense temporal annotations, only video-level labels are sufficient for learning.
snehashismajhi/TokenLearner
TensorFlow implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"