68 citations · 182 across the 14 of their papers we have counts for
14 papers
Open-World Object Detection via Discriminative Class Prototype Learning
Jinan Yu, Liyan Ma, Zhenglin Li +2
Open-world object detection (OWOD) is a challenging problem that combines object detection with incremental learning and open-set learning. Compared to standard object detection, t…
Imperceptible Adversarial Examples for Fake Image Detection
Quanyu Liao, Yuezun Li, Xin Wang +6
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society. Many methods have been proposed to detect fake im…
Lvio-Fusion: A Self-adaptive Multi-sensor Fusion SLAM Framework Using Actor-critic Method
Yupeng Jia, Haiyong Luo, Fang Zhao +5
State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a pr…
Rethinking the constraints of multimodal fusion: case study in Weakly-Supervised Audio-Visual Video Parsing
Jianning Wu, Zhuqing Jiang, Shiping Wen +2
For multimodal tasks, a good feature extraction network should extract information as much as possible and ensure that the extracted feature embedding and other modal feature embed…
Taylor saves for later: disentanglement for video prediction using Taylor representation
Ting Pan, Zhuqing Jiang, Jianan Han +3
Video prediction is a challenging task with wide application prospects in meteorology and robot systems. Existing works fail to trade off short-term and long-term prediction perfor…
Bridge the Vision Gap from Field to Command: A Deep Learning Network Enhancing Illumination and Details
Zhuqing Jiang, Chang Liu, Ya'nan Wang +4
With the goal of tuning up the brightness, low-light image enhancement enjoys numerous applications, such as surveillance, remote sensing and computational photography. Images capt…
Shed Various Lights on a Low-Light Image: Multi-Level Enhancement Guided by Arbitrary References
Ya'nan Wang, Zhuqing Jiang, Chang Liu +3
It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. H…
A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement
Zhuqing Jiang, Haotian Li, Liangjie Liu +2
Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. However,…
Low-Rank Subspace Representation from Optimal Coded-Aperture for Unsupervised Classification of Hyperspectral Imagery
Jianchen Zhu
This paper aims at developing a clustering approach with spectral images directly from the compressive measurements of coded aperture snapshot spectral imager (CASSI). Assuming tha…
Domain Generalization via Optimal Transport with Metric Similarity Learning
Fan Zhou, Zhuqing Jiang, Changjian Shui +2
Generalizing knowledge to unseen domains, where data and labels are unavailable, is crucial for machine learning models. We tackle the domain generalization problem to learn from m…
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification
Devesh Walawalkar, Zhiqiang Shen, Zechun Liu +1
Convolutional neural networks (CNN) are capable of learning robust representation with different regularization methods and activations as convolutional layers are spatially correl…
Learning Target-oriented Dual Attention for Robust RGB-T Tracking
Rui Yang, Yabin Zhu, Xiao Wang +2
RGB-Thermal object tracking attempt to locate target object using complementary visual and thermal infrared data. Existing RGB-T trackers fuse different modalities by robust featur…
Pyramid Real Image Denoising Network
Yiyun Zhao, Zhuqing Jiang, Aidong Men +1
While deep Convolutional Neural Networks (CNNs) have shown extraordinary capability of modelling specific noise and denoising, they still perform poorly on real-world noisy images.…
Channel Attention and Multi-level Features Fusion for Single Image Super-Resolution
Yue Lu, Yun Zhou, Zhuqing Jiang +2
Convolutional neural networks (CNNs) have demonstrated superior performance in super-resolution (SR). However, most CNN-based SR methods neglect the different importance among feat…