157 citations · 1.3k across the 97 of their papers we have counts for
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Learning to Dub Movies via Hierarchical Prosody Models
Gaoxiang Cong, Liang Li, Yuankai Qi +6
Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone V2C) task aims to generate speeches that match the speaker's emotion…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +4
Weakly supervised Referring Expression Grounding (REG) aims to ground a particular target in an image described by a language expression while lacking the correspondence between ta…
Enhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images
Naishan Zheng, Jie Huang, Qi Zhu +3
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic. Motivated by this, several personalized enhancement methods have been…
Rank Diminishing in Deep Neural Networks
Ruili Feng, Kecheng Zheng, Yukun Huang +3
The rank of neural networks measures information flowing across layers. It is an instance of a key structural condition that applies across broad domains of machine learning. In pa…
Label Noise-Resistant Mean Teaching for Weakly Supervised Fake News Detection
Jingyi Xie, Jiawei Liu, Zheng-Jun Zha
Fake news spreads at an unprecedented speed, reaches global audiences and poses huge risks to users and communities. Most existing fake news detection algorithms focus on building…
Automatic Relation-aware Graph Network Proliferation
Shaofei Cai, Liang Li, Xinzhe Han +3
Graph neural architecture search has sparked much attention as Graph Neural Networks (GNNs) have shown powerful reasoning capability in many relational tasks. However, the currentl…
Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng +4
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous wor…
Degradation-agnostic Correspondence from Resolution-asymmetric Stereo
Xihao Chen, Zhiwei Xiong, Zhen Cheng +3
In this paper, we study the problem of stereo matching from a pair of images with different resolutions, e.g., those acquired with a tele-wide camera system. Due to the difficulty…
Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency
Zhenhuan Liu, Liang Li, Huajie Jiang +4
In recent years, creative content generations like style transfer and neural photo editing have attracted more and more attention. Among these, cartoonization of real-world scenes…
Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment
Jiayu Xiao, Liang Li, Chaofei Wang +2
Training a generative adversarial network (GAN) with limited data has been a challenging task. A feasible solution is to start with a GAN well-trained on a large scale source domai…
FAMLP: A Frequency-Aware MLP-Like Architecture For Domain Generalization
Kecheng Zheng, Yang Cao, Kai Zhu +2
MLP-like models built entirely upon multi-layer perceptrons have recently been revisited, exhibiting the comparable performance with transformers. It is one of most promising archi…
ProgressiveMotionSeg: Mutually Reinforced Framework for Event-Based Motion Segmentation
Jinze Chen, Yang Wang, Yang Cao +2
Dynamic Vision Sensor (DVS) can asynchronously output the events reflecting apparent motion of objects with microsecond resolution, and shows great application potential in monitor…
Location-Free Camouflage Generation Network
Yangyang Li, Wei Zhai, Yang Cao +1
Camouflage is a common visual phenomenon, which refers to hiding the foreground objects into the background images, making them briefly invisible to the human eye. Previous work ha…
Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning
Kai Zhu, Wei Zhai, Yang Cao +2
Non-exemplar class-incremental learning is to recognize both the old and new classes when old class samples cannot be saved. It is a challenging task since representation optimizat…
Modality-Adaptive Mixup and Invariant Decomposition for RGB-Infrared Person Re-Identification
Zhipeng Huang, Jiawei Liu, Liang Li +2
RGB-infrared person re-identification is an emerging cross-modality re-identification task, which is very challenging due to significant modality discrepancy between RGB and infrar…
Debiased Batch Normalization via Gaussian Process for Generalizable Person Re-Identification
Jiawei Liu, Zhipeng Huang, Liang Li +2
Generalizable person re-identification aims to learn a model with only several labeled source domains that can perform well on unseen domains. Without access to the unseen domain,…