Beerel, Peter A.

14 publications

WACV 2025 MaskVD: Region Masking for Efficient Video Object Detection Sreetama Sarkar, Gourav Datta, Souvik Kundu, Kai Zheng, Chirayata Bhattacharyya, Peter A. Beerel

CVPRW 2024 Block Selective Reprogramming for On-Device Training of Vision Transformers Sreetama Sarkar, Souvik Kundu, Kai Zheng, Peter A. Beerel

CVPRW 2024 DIA: Diffusion Based Inverse Network Attack on Collaborative Inference Dake Chen, Shiduo Li, Yuke Zhang, Chenghao Li, Souvik Kundu, Peter A. Beerel

CVPRW 2024 RLNet: Robust Linearized Networks for Efficient Private Inference Sreetama Sarkar, Souvik Kundu, Peter A. Beerel

WACV 2023 Enabling ISPless Low-Power Computer Vision Gourav Datta, Zeyu Liu, Zihan Yin, Linyu Sun, Akhilesh R. Jaiswal, Peter A. Beerel

WACV 2023 FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-Off Between Accuracy and Robustness Souvik Kundu, Sairam Sundaresan, Massoud Pedram, Peter A. Beerel

ICCVW 2023 FireFly: A Synthetic Dataset for Ember Detection in Wildfire Yue Hu, Xinan Ye, Yifei Liu, Souvik Kundu, Gourav Datta, Srikar Mutnuri, Namo Asavisanu, Nora Ayanian, Konstantinos Psounis, Peter A. Beerel

CVPRW 2023 Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference Souvik Kundu, Yuke Zhang, Dake Chen, Peter A. Beerel

ICCV 2023 SAL-ViT: Towards Latency Efficient Private Inference on ViT Using Selective Attention Search with a Learnable SoftMax Approximation Yuke Zhang, Dake Chen, Souvik Kundu, Chenghao Li, Peter A. Beerel

WACV 2023 Self-Attentive Pooling for Efficient Deep Learning Fang Chen, Gourav Datta, Souvik Kundu, Peter A. Beerel

ECCVW 2022 Towards Energy-Efficient Hyperspectral Image Processing Inside Camera Pixels Gourav Datta, Zihan Yin, Ajey P. Jacob, Akhilesh R. Jaiswal, Peter A. Beerel

ICCV 2021 HIRE-SNN: Harnessing the Inherent Robustness of Energy-Efficient Deep Spiking Neural Networks by Training with Crafted Input Noise Souvik Kundu, Massoud Pedram, Peter A. Beerel

WACV 2021 Spike-Thrift: Towards Energy-Efficient Deep Spiking Neural Networks by Limiting Spiking Activity via Attention-Guided Compression Souvik Kundu, Gourav Datta, Massoud Pedram, Peter A. Beerel

ACML 2020 Deep-N-Cheap: An Automated Search Framework for Low Complexity Deep Learning Sourya Dey, Saikrishna C. Kanala, Keith M. Chugg, Peter A. Beerel