553 citations · 1.6k across the 70 of their papers we have counts for
15 papers · 1 filter
Towards Unified INT8 Training for Convolutional Neural Network
Feng Zhu, Ruihao Gong, Fengwei Yu +5
Recently low-bit (e.g., 8-bit) network quantization has been extensively studied to accelerate the inference. Besides inference, low-bit training with quantized gradients can furth…
Fast and Incremental Loop Closure Detection Using Proximity Graphs
Shan An, Guangfu Che, Fangru Zhou +3
Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems. The frequently u…
Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
Yuqing Ma, Xianglong Liu, Shihao Bai +4
Recently deep neutral networks have achieved promising performance for filling large missing regions in image inpainting tasks. They usually adopted the standard convolutional arch…
Balanced Binary Neural Networks with Gated Residual
Mingzhu Shen, Xianglong Liu, Ruihao Gong +1
Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the acc…
Attention Convolutional Binary Neural Tree for Fine-Grained Visual Categorization
Ruyi Ji, Longyin Wen, Libo Zhang +5
Fine-grained visual categorization (FGVC) is an important but challenging task due to high intra-class variances and low inter-class variances caused by deformation, occlusion, ill…
Forward and Backward Information Retention for Accurate Binary Neural Networks
Haotong Qin, Ruihao Gong, Xianglong Liu +4
Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many bina…
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Aishan Liu, Xianglong Liu, Chongzhi Zhang +3
In practice, deep neural networks have been found to be vulnerable to various types of noise, such as adversarial examples and corruption. Various adversarial defense methods have…
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
Chongzhi Zhang, Aishan Liu, Xianglong Liu +4
Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Despite the potential risk they…
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks
Hang Yu, Aishan Liu, Xianglong Liu +5
Adversarial images are designed to mislead deep neural networks (DNNs), attracting great attention in recent years. Although several defense strategies achieved encouraging robustn…
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
Ruihao Gong, Xianglong Liu, Shenghu Jiang +5
Hardware-friendly network quantization (e.g., binary/uniform quantization) can efficiently accelerate the inference and meanwhile reduce memory consumption of the deep neural netwo…
Query-Adaptive Hash Code Ranking for Large-Scale Multi-View Visual Search
Xianglong Liu, Lei Huang, Cheng Deng +2
Hash based nearest neighbor search has become attractive in many applications. However, the quantization in hashing usually degenerates the discriminative power when using Hamming…
Active Multi-Kernel Domain Adaptation for Hyperspectral Image Classification
Cheng Deng, Xianglong Liu, Chao Li +1
Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information usi…
Active Transfer Learning Network: A Unified Deep Joint Spectral-Spatial Feature Learning Model For Hyperspectral Image Classification
Cheng Deng, Yumeng Xue, Xianglong Liu +2
Deep learning has recently attracted significant attention in the field of hyperspectral images (HSIs) classification. However, the construction of an efficient deep neural network…
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
Cheng Deng, Zhaojia Chen, Xianglong Liu +2
Given the benefits of its low storage requirements and high retrieval efficiency, hashing has recently received increasing attention. In particular,cross-modal hashing has been wid…
Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval
Chao Li, Cheng Deng, Lei Wang +2
In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Be…