activity
20202022
most citedSR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization

113 citations · 181 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV2022113 cited

SR-GNN: Spatial Relation-aware Graph Neural Network for Fine-Grained Image Categorization

Asish Bera, Zachary Wharton, Yonghuai Liu +2

Over the past few years, a significant progress has been made in deep convolutional neural networks (CNNs)-based image recognition. This is mainly due to the strong ability of such…

cs.RO2022

Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics

Swagat Kumar, Hayden Sampson, Ardhendu Behera

This paper presents a benchmarking study of some of the state-of-the-art reinforcement learning algorithms used for solving two simulated vision-based robotics problems. The algori…

cs.CV202149 cited

Attend and Guide (AG-Net): A Keypoints-driven Attention-based Deep Network for Image Recognition

Asish Bera, Zachary Wharton, Yonghuai Liu +2

This paper presents a novel keypoints-based attention mechanism for visual recognition in still images. Deep Convolutional Neural Networks (CNNs) for recognizing images with distin…

cs.CV2021

An attention-driven hierarchical multi-scale representation for visual recognition

Zachary Wharton, Ardhendu Behera, Asish Bera

Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extrac…

cs.CV20211 cited

Coarse Temporal Attention Network (CTA-Net) for Driver's Activity Recognition

Zachary Wharton, Ardhendu Behera, Yonghuai Liu +1

There is significant progress in recognizing traditional human activities from videos focusing on highly distinctive actions involving discriminative body movements, body-object an…

cs.CV20211 cited

Context-aware Attentional Pooling (CAP) for Fine-grained Visual Classification

Ardhendu Behera, Zachary Wharton, Pradeep Hewage +1

Deep convolutional neural networks (CNNs) have shown a strong ability in mining discriminative object pose and parts information for image recognition. For fine-grained recognition…