Search Results for author: Ardhendu Behera

Found 11 papers, 6 papers with code

High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics

no code implementations17 Sep 2024 Aditya Humnabadkar, Arindam Sikdar, Benjamin Cave, Huaizhong Zhang, Paul Bakaki, Ardhendu Behera

We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts.

Autonomous Driving

Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning

1 code implementation23 Aug 2022 Jinkui Hao, Ting Shen, Xueli Zhu, Yonghuai Liu, Ardhendu Behera, Dan Zhang, Bang Chen, Jiang Liu, Jiong Zhang, Yitian Zhao

Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making.

Classification Decision Making +1

Benchmarking Deep Reinforcement Learning Algorithms for Vision-based Robotics

no code implementations11 Jan 2022 Swagat Kumar, Hayden Sampson, Ardhendu Behera

To the best of our knowledge, such a benchmarking study is not available for the above two vision-based robotics problems making it a novel contribution in the field.

Benchmarking reinforcement-learning +2

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

1 code implementation23 Oct 2021 Asish Bera, Zachary Wharton, Yonghuai Liu, Nik Bessis, Ardhendu Behera

We address this by proposing an end-to-end CNN model, which learns meaningful features linking fine-grained changes using our novel attention mechanism.

Fine-Grained Image Recognition Image Classification

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

no code implementations17 Jan 2021 Zachary Wharton, Ardhendu Behera, Yonghuai Liu, Nik Bessis

Our model is named Coarse Temporal Attention Network (CTA-Net), in which coarse temporal branches are introduced in a trainable glimpse network.

Activity Recognition

Unsupervised Monocular Depth Estimation for Night-time Images using Adversarial Domain Feature Adaptation

1 code implementation ECCV 2020 Madhu Vankadari, Sourav Garg, Anima Majumder, Swagat Kumar, Ardhendu Behera

We propose to solve this problem by posing it as a domain adaptation problem where a network trained with day-time images is adapted to work for night-time images.

Decoder Depth Prediction +4

Orderly Disorder in Point Cloud Domain

1 code implementation21 Aug 2020 Morteza Ghahremani, Bernard Tiddeman, Yonghuai Liu, Ardhendu Behera

Our method extracts the deep patterns inside a 3D object via creating a dynamic link to seek the most stable patterns and at once, throws away the unstable ones.

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