2 citations · 3 across the 7 of their papers we have counts for
7 papers
Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects
Xiaoheng Jiang, Feng Yan, Yang Lu +6
Surface defect inspection plays an important role in the process of industrial manufacture and production. Though Convolutional Neural Network (CNN) based defect inspection methods…
Spectral Aware Softmax for Visible-Infrared Person Re-Identification
Lei Tan, Pingyang Dai, Qixiang Ye +3
Visible-infrared person re-identification (VI-ReID) aims to match specific pedestrian images from different modalities. Although suffering an extra modality discrepancy, existing m…
Long-Range Zero-Shot Generative Deep Network Quantization
Yan Luo, Yangcheng Gao, Zhao Zhang +3
Quantization approximates a deep network model with floating-point numbers by the one with low bit width numbers, in order to accelerate inference and reduce computation. Quantizin…
Seeing Through the Noisy Dark: Towards Real-world Low-Light Image Enhancement and Denoising
Jiahuan Ren, Zhao Zhang, Richang Hong +3
Low-light image enhancement (LLIE) aims at improving the illumination and visibility of dark images with lighting noise. To handle the real-world low-light images often with heavy…
OSIC: A New One-Stage Image Captioner Coined
Bo Wang, Zhao Zhang, Mingbo Zhao +3
Mainstream image caption models are usually two-stage captioners, i.e., calculating object features by pre-trained detector, and feeding them into a language model to generate text…
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
Zhou Yu, Zitian Jin, Jun Yu +3
Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA). Nevertheless, Transformer-based VQA models are usually dee…
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
Chao Sun, Zhedong Zheng, Xiaohan Wang +2
The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training a…