Consistency driven Sequential Transformers Attention Model for Partially Observable Scenes

Author:

Rangrej Samrudhdhi B.1,Srinidhi Chetan L.2,Clark James J.1

Affiliation:

1. McGill University,Canada

2. Sunnybrook Research Institute, University of Toronto,Canada

Publisher

IEEE

Reference54 articles.

1. Fixmatch: Simplifying semi-supervised learning with consistency and confidence;sohn;ArXiv Preprint,2020

2. Attend and Segment: Attention Guided Active Semantic Segmentation

3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

4. Learning to Zoom: A Saliency-Based Sampling Layer for Neural Networks

5. Dynamicvit: Efficient vision transformers with dynamic token sparsification;rao;ArXiv Preprint,2021

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1. Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

2. GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action Prediction;2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2023-01

3. BiCOD: A Camouflaged Object Detection Method Directed by Cognitive Attention;IEEE Sensors Journal;2023

4. HAWK-Net: Hierarchical Attention Weighted Top-K Network for High-resolution Image Classification;Journal of Information Processing;2023

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