25 citations · 108 across the 23 of their papers we have counts for
23 papers
Pick-and-Draw: Training-free Semantic Guidance for Text-to-Image Personalization
Henglei Lv, Jiayu Xiao, Liang Li +1
Diffusion-based text-to-image personalization have achieved great success in generating subjects specified by users among various contexts. Even though, existing finetuning-based m…
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation
Jiayu Xiao, Henglei Lv, Liang Li +2
Recent text-to-image (T2I) diffusion models have achieved remarkable progress in generating high-quality images given text-prompts as input. However, these models fail to convey ap…
Self-supervised Cross-view Representation Reconstruction for Change Captioning
Yunbin Tu, Liang Li, Li Su +3
Change captioning aims to describe the difference between a pair of similar images. Its key challenge is how to learn a stable difference representation under pseudo changes caused…
Neighborhood Contrastive Transformer for Change Captioning
Yunbin Tu, Liang Li, Li Su +2
Change captioning is to describe the semantic change between a pair of similar images in natural language. It is more challenging than general image captioning, because it requires…
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…
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…
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…
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…
IR-GAN: Image Manipulation with Linguistic Instruction by Increment Reasoning
Zhenhuan Liu, Jincan Deng, Liang Li +4
Conditional image generation is an active research topic including text2image and image translation. Recently image manipulation with linguistic instruction brings new challenges o…
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…
Calibrated Feature Decomposition for Generalizable Person Re-Identification
Kecheng Zheng, Jiawei Liu, Wei Wu +2
Existing disentangled-based methods for generalizable person re-identification aim at directly disentangling person representations into domain-relevant interference and identity-r…
RNet:Relation-embedded Representation Reconstruction Network for Change Captioning
Yunbin Tu, Liang Li, Chenggang Yan +2
Change captioning is to use a natural language sentence to describe the fine-grained disagreement between two similar images. Viewpoint change is the most typical distractor in thi…
Edge-featured Graph Neural Architecture Search
Shaofei Cai, Liang Li, Xinzhe Han +2
Graph neural networks (GNNs) have been successfully applied to learning representation on graphs in many relational tasks. Recently, researchers study neural architecture search (N…
Multi-Modulation Network for Audio-Visual Event Localization
Hao Wang, Zheng-Jun Zha, Liang Li +2
We study the problem of localizing audio-visual events that are both audible and visible in a video. Existing works focus on encoding and aligning audio and visual features at the…
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain Adaptation
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain.…
Rethinking Graph Neural Architecture Search from Message-passing
Shaofei Cai, Liang Li, Jincan Deng +4
Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore diffe…
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Dechao Meng, Liang Li, Xuejing Liu +6
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario. The main challenges of this task are the large intra-instance dista…
State-Relabeling Adversarial Active Learning
Beichen Zhang, Liang Li, Shijie Yang +3
Active learning is to design label-efficient algorithms by sampling the most representative samples to be labeled by an oracle. In this paper, we propose a state relabeling adversa…
Real-world Person Re-Identification via Degradation Invariance Learning
Yukun Huang, Zheng-Jun Zha, Xueyang Fu +2
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e.g., low-resolution, weak illumination, blurring and adverse weather. On…
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations
Shuhao Cui, Shuhui Wang, Junbao Zhuo +3
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary w…
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +3
Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image regi…
Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Xuejing Liu, Liang Li, Shuhui Wang +3
Weakly supervised referring expression grounding aims at localizing the referential object in an image according to the linguistic query, where the mapping between the referential…
Image Classification base on PCA of Multi-view Deep Representation
Yaoqi Sun, Liang Li, Liang Zheng +3
In the age of information explosion, image classification is the key technology of dealing with and organizing a large number of image data. Currently, the classical image classifi…