Search Results for author:
Found 10 papers, 5 papers with code
Visual Context-Aware Person Fall Detection
During training, pixel-based transformations are applied to segmented objects, and the models are then evaluated on raw images without segmentation.
CL-MAE: Curriculum-Learned Masked Autoencoders
In this paper, we propose a curriculum learning approach that updates the masking strategy to continually increase the complexity of the self-supervised reconstruction task.
Self-Supervised Masked Convolutional Transformer Block for Anomaly Detection
In this work, we extend our previous self-supervised predictive convolutional attentive block (SSPCAB) with a 3D masked convolutional layer, a transformer for channel-wise attention, as well as a novel self-supervised objective based on Huber loss.
Ranked #4 on
Anomaly Detection
on CUHK Avenue
SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection
A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature.
Ranked #2 on
Anomaly Detection
on CUHK Avenue
Video Transformers: A Survey
Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video.
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Our block is equipped with a loss that minimizes the reconstruction error with respect to the masked area in the receptive field.
Ranked #1 on
Anomaly Detection
on CUHK Avenue
(TBDC metric)
Real-World Super-Resolution of Face-Images from Surveillance Cameras
Experimental results on both real and artificially corrupted face images show that our method results in more detailed reconstructions with less noise compared to existing State-of-the-Art (SoTA) methods.
SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos
In this work, we propose SoccerNet-v2, a novel large-scale corpus of manual annotations for the SoccerNet video dataset, along with open challenges to encourage more research in soccer understanding and broadcast production.
Ranked #1 on
Camera shot segmentation
on SoccerNet-v2
Effective Fusion of Deep Multitasking Representations for Robust Visual Tracking
Then, the proposed method extracts deep semantic information from a fully convolutional FEN and fuses it with the best ResNet-based feature maps to strengthen the target representation in the learning process of continuous convolution filters.
A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition
This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task.
