34 citations · 58 across the 10 of their papers we have counts for
11 papers
MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
Xiaohan Zhang, Yuqing Wen, Junlin Chen +9
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mai…
Scalable In-Context Q-Learning
Jinmei Liu, Fuhong Liu, Zhenhong Sun +6
Recent advancements in language models have demonstrated remarkable in-context learning abilities, prompting the exploration of in-context reinforcement learning (ICRL) to extend t…
Fast Disentangled Slim Tensor Learning for Multi-view Clustering
Deng Xu, Chao Zhang, Zechao Li +2
Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing…
DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark
Haoxing Chen, Yan Hong, Zizheng Huang +8
Recently, video generation techniques have advanced rapidly. Given the popularity of video content on social media platforms, these models intensify concerns about the spread of fa…
Segment Anything Model Meets Image Harmonization
Haoxing Chen, Yaohui Li, Zhangxuan Gu +3
Image harmonization is a crucial technique in image composition that aims to seamlessly match the background by adjusting the foreground of composite images. Current methods adopt…
Contrastive Latent Space Reconstruction Learning for Audio-Text Retrieval
Kaiyi Luo, Xulong Zhang, Jianzong Wang +3
Cross-modal retrieval (CMR) has been extensively applied in various domains, such as multimedia search engines and recommendation systems. Most existing CMR methods focus on image-…
Hierarchical Dynamic Image Harmonization
Haoxing Chen, Zhangxuan Gu, Yaohui Li +4
Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global…
Sparse Spatial Transformers for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose use…
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Yaohui Li, Huaxiong Li, Haoxing Chen +1
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data. Recent metric-based frameworks tend to represent a support class b…
Multi-level Metric Learning for Few-shot Image Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Few-shot learning is devoted to training a model on few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, usin…
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying…