343 citations · 460 across the 31 of their papers we have counts for
14 papers · 1 filter
Make-A-Storyboard: A General Framework for Storyboard with Disentangled and Merged Control
Sitong Su, Litao Guo, Lianli Gao +2
Story Visualization aims to generate images aligned with story prompts, reflecting the coherence of storybooks through visual consistency among characters and scenes.Whereas curren…
ALF: Adaptive Label Finetuning for Scene Graph Generation
Qishen Chen, Jianzhi Liu, Xinyu Lyu +3
Scene Graph Generation (SGG) endeavors to predict the relationships between subjects and objects in a given image. Nevertheless, the long-tail distribution of relations often leads…
JoReS-Diff: Joint Retinex and Semantic Priors in Diffusion Model for Low-light Image Enhancement
Yuhui Wu, Guoqing Wang, Zhiwen Wang +5
Low-light image enhancement (LLIE) has achieved promising performance by employing conditional diffusion models. Despite the success of some conditional methods, previous methods m…
CUCL: Codebook for Unsupervised Continual Learning
Chen Cheng, Jingkuan Song, Xiaosu Zhu +3
The focus of this study is on Unsupervised Continual Learning (UCL), as it presents an alternative to Supervised Continual Learning which needs high-quality manual labeled data. Th…
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation
Dongshen Han, Chaoning Zhang, Sheng Zheng +3
As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored. This works investigat…
Part-Aware Transformer for Generalizable Person Re-identification
Hao Ni, Yuke Li, Lianli Gao +2
Domain generalization person re-identification (DG-ReID) aims to train a model on source domains and generalize well on unseen domains. Vision Transformer usually yields better gen…
From Global to Local: Multi-scale Out-of-distribution Detection
Ji Zhang, Lianli Gao, Bingguang Hao +3
Out-of-distribution (OOD) detection aims to detect "unknown" data whose labels have not been seen during the in-distribution (ID) training process. Recent progress in representatio…
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
Tianshi Wang, Fengling Li, Lei Zhu +3
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities…
Informative Scene Graph Generation via Debiasing
Lianli Gao, Xinyu Lyu, Yuyu Guo +5
Scene graph generation aims to detect visual relationship triplets, (subject, predicate, object). Due to biases in data, current models tend to predict common predicates, e.g. "on"…
A Universal Unbiased Method for Classification from Aggregate Observations
Zixi Wei, Lei Feng, Bo Han +4
In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances,…
Instance-Variant Loss with Gaussian RBF Kernel for 3D Cross-modal Retriveal
Zhitao Liu, Zengyu Liu, Jiwei Wei +4
3D cross-modal retrieval is gaining attention in the multimedia community. Central to this topic is learning a joint embedding space to represent data from different modalities, su…
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Jin Sun, Xiaoshuang Shi, Zhiyuan Wang +3
Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convol…
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
Jinghan Ru, Jun Tian, Zhekai Du +3
Multimedia applications are often associated with cross-domain knowledge transfer, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Open Set Doma…
A Comprehensive Survey on Source-free Domain Adaptation
Zhiqi Yu, Jingjing Li, Zhekai Du +2
Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the…