65 citations · 221 across the 23 of their papers we have counts for
25 papers
SC-Diff: Semantically Calibrated Diffusion for Visible-to-Infrared Image Translation
Junyin Zhang, Siyu Huang, Jianxiong Ye +4
Visible-to-infrared image translation provides a practical way to expand infrared training data using abundant visible images. Diffusion models are promising for this task because…
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts
Weibin Liao, Haoyi Xiong, Qingzhong Wang +6
While self-supervised learning (SSL) algorithms have been widely used to pre-train deep models, few efforts [11] have been done to improve representation learning of X-ray image an…
Domain-Scalable Unpaired Image Translation via Latent Space Anchoring
Siyu Huang, Jie An, Donglai Wei +3
Unpaired image-to-image translation (UNIT) aims to map images between two visual domains without paired training data. However, given a UNIT model trained on certain domains, it is…
Temporal Output Discrepancy for Loss Estimation-based Active Learning
Siyu Huang, Tianyang Wang, Haoyi Xiong +3
While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of dat…
QuantArt: Quantizing Image Style Transfer Towards High Visual Fidelity
Siyu Huang, Jie An, Donglai Wei +2
The mechanism of existing style transfer algorithms is by minimizing a hybrid loss function to push the generated image toward high similarities in both content and style. However,…
Making Your First Choice: To Address Cold Start Problem in Vision Active Learning
Liangyu Chen, Yutong Bai, Siyu Huang +4
Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first. However, we uncover a striking contradiction to th…
Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN
Leander Lauenburg, Zudi Lin, Ruihan Zhang +6
Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segme…
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…
FINO: Flow-based Joint Image and Noise Model
Lanqing Guo, Siyu Huang, Haosen Liu +1
One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements. To tackle such an ill-posed inv…
Cross-Model Consensus of Explanations and Beyond for Image Classification Models: An Empirical Study
Xuhong Li, Haoyi Xiong, Siyu Huang +2
Existing interpretation algorithms have found that, even deep models make the same and right predictions on the same image, they might rely on different sets of input features for…
AutoGCL: Automated Graph Contrastive Learning via Learnable View Generators
Yihang Yin, Qingzhong Wang, Siyu Huang +2
Contrastive learning has been widely applied to graph representation learning, where the view generators play a vital role in generating effective contrastive samples. Most of the…
Semi-Supervised Active Learning with Temporal Output Discrepancy
Siyu Huang, Tianyang Wang, Haoyi Xiong +2
While deep learning succeeds in a wide range of tasks, it highly depends on the massive collection of annotated data which is expensive and time-consuming. To lower the cost of dat…
ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement
Rongkai Zhang, Lanqing Guo, Siyu Huang +1
Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can…
Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples
Xuanyu Wu, Xuhong Li, Haoyi Xiong +3
Training images with data transformations have been suggested as contrastive examples to complement the testing set for generalization performance evaluation of deep neural network…
ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows
Jie An, Siyu Huang, Yibing Song +3
Universal style transfer retains styles from reference images in content images. While existing methods have achieved state-of-the-art style transfer performance, they are not awar…
BM-NAS: Bilevel Multimodal Neural Architecture Search
Yihang Yin, Siyu Huang, Xiang Zhang
Deep neural networks (DNNs) have shown superior performances on various multimodal learning problems. However, it often requires huge efforts to adapt DNNs to individual multimodal…
SBAT: Video Captioning with Sparse Boundary-Aware Transformer
Tao Jin, Siyu Huang, Ming Chen +2
In this paper, we focus on the problem of applying the transformer structure to video captioning effectively. The vanilla transformer is proposed for uni-modal language generation…
Generating Person Images with Appearance-aware Pose Stylizer
Siyu Huang, Haoyi Xiong, Zhi-Qi Cheng +5
Generation of high-quality person images is challenging, due to the sophisticated entanglements among image factors, e.g., appearance, pose, foreground, background, local details,…
Parameter-Free Style Projection for Arbitrary Style Transfer
Siyu Huang, Haoyi Xiong, Tianyang Wang +5
Arbitrary image style transfer is a challenging task which aims to stylize a content image conditioned on arbitrary style images. In this task the feature-level content-style trans…
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning
Tao Jin, Siyu Huang, Yingming Li +1
This paper addresses the challenging task of video captioning which aims to generate descriptions for video data. Recently, the attention-based encoder-decoder structures have been…
Text Guided Person Image Synthesis
Xingran Zhou, Siyu Huang, Bin Li +3
This paper presents a novel method to manipulate the visual appearance (pose and attribute) of a person image according to natural language descriptions. Our method can be boiled d…
Perceiving Physical Equation by Observing Visual Scenarios
Siyu Huang, Zhi-Qi Cheng, Xi Li +3
Inferring universal laws of the environment is an important ability of human intelligence as well as a symbol of general AI. In this paper, we take a step toward this goal such tha…
Stacked Pooling: Improving Crowd Counting by Boosting Scale Invariance
Siyu Huang, Xi Li, Zhi-Qi Cheng +2
In this work, we explore the cross-scale similarity in crowd counting scenario, in which the regions of different scales often exhibit high visual similarity. This feature is unive…
GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning
Siyu Huang, Xi Li, Zhi-Qi Cheng +2
A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures. Typically, the conventional deep multi-attribute learning appr…