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20082022
most citedA Survey of FPGA-Based Neural Network Accelerator

140 citations · 719 across the 50 of their papers we have counts for

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10 papers · 1 filter

cs.CV2022★ 2 cited

Cross-layer Attention Network for Fine-grained Visual Categorization

Ranran Huang, Yu Wang, Huazhong Yang

Learning discriminative representations for subtle localized details plays a significant role in Fine-grained Visual Categorization (FGVC). Compared to previous attention-based wor…

cs.CV2021★ 2 cited

Learning Efficient Multi-Agent Cooperative Visual Exploration

Chao Yu, Xinyi Yang, Jiaxuan Gao +3

We tackle the problem of cooperative visual exploration where multiple agents need to jointly explore unseen regions as fast as possible based on visual signals. Classical planning…

cs.CV2020★ 3 cited

Attentional Separation-and-Aggregation Network for Self-supervised Depth-Pose Learning in Dynamic Scenes

Feng Gao, Jincheng Yu, Hao Shen +2

Learning depth and ego-motion from unlabeled videos via self-supervision from epipolar projection can improve the robustness and accuracy of the 3D perception and localization of v…

cs.CV2020★ 39 cited

Physical Adversarial Attack on Vehicle Detector in the Carla Simulator

Tong Wu, Xuefei Ning, Wenshuo Li +3

In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on ve…

cs.CV2020★ 31 cited

Evaluating Efficient Performance Estimators of Neural Architectures

Xuefei Ning, Changcheng Tang, Wenshuo Li +4

Conducting efficient performance estimations of neural architectures is a major challenge in neural architecture search (NAS). To reduce the architecture training costs in NAS, one…

cs.CV2020★ 9 cited

DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

Xuefei Ning, Tianchen Zhao, Wenshuo Li +3

Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key pro…