149 citations · 360 across the 29 of their papers we have counts for
7 papers · 1 filter
Heuristic Rank Selection with Progressively Searching Tensor Ring Network
Nannan Li, Yu Pan, Yaran Chen +3
Recently, Tensor Ring Networks (TRNs) have been applied in deep networks, achieving remarkable successes in compression ratio and accuracy. Although highly related to the performan…
BNAS-v2: Memory-efficient and Performance-collapse-prevented Broad Neural Architecture Search
Zixiang Ding, Yaran Chen, Nannan Li +1
In this paper, we propose BNAS-v2 to further improve the efficiency of NAS, embodying both superiorities of BCNN simultaneously. To mitigate the unfair training issue of BNAS, we e…
ContourRend: A Segmentation Method for Improving Contours by Rendering
Junwen Chen, Yi Lu, Yaran Chen +2
A good object segmentation should contain clear contours and complete regions. However, mask-based segmentation can not handle contour features well on a coarse prediction grid, th…
BiFNet: Bidirectional Fusion Network for Road Segmentation
Haoran Li, Yaran Chen, Qichao Zhang +1
Multi-sensor fusion-based road segmentation plays an important role in the intelligent driving system since it provides a drivable area. The existing mainstream fusion method is ma…
ModuleNet: Knowledge-inherited Neural Architecture Search
Yaran Chen, Ruiyuan Gao, Fenggang Liu +1
Although Neural Architecture Search (NAS) can bring improvement to deep models, they always neglect precious knowledge of existing models. The computation and time costing property…
Graph-FCN for image semantic segmentation
Yi Lu, Yaran Chen, Dongbin Zhao +1
Semantic segmentation with deep learning has achieved great progress in classifying the pixels in the image. However, the local location information is usually ignored in the high-…
Graph Attention Memory for Visual Navigation
Dong Li, Qichao Zhang, Dongbin Zhao +5
Visual navigation in complex environments is inefficient with traditional reactive policy or general-purposed recurrent policy. To address the long-term memory issue, this paper pr…