1 citations · 1 across the 9 of their papers we have counts for
10 papers
WRF4CIR: Weight-Regularized Fine-Tuning Network for Composed Image Retrieval
Yizhuo Xu, Chaojian Yu, Yuanjie Shao +3
Composed Image Retrieval (CIR) task aims to retrieve target images based on reference images and modification texts. Current CIR methods primarily rely on fine-tuning vision-langua…
Mutually Causal Semantic Distillation Network for Zero-Shot Learning
Shiming Chen, Shuhuang Chen, Guo-Sen Xie +1
Zero-shot learning (ZSL) aims to recognize the unseen classes in the open-world guided by the side-information (e.g., attributes). Its key task is how to infer the latent semantic…
VII: Visual Instruction Injection for Jailbreaking Image-to-Video Generation Models
Bowen Zheng, Yongli Xiang, Ziming Hong +4
Image-to-Video (I2V) generation models, which condition video generation on reference images, have shown emerging visual instruction-following capability, allowing certain visual c…
Prototype-Guided Curriculum Learning for Zero-Shot Learning
Lei Wang, Shiming Chen, Guo-Sen Xie +4
In Zero-Shot Learning (ZSL), embedding-based methods enable knowledge transfer from seen to unseen classes by learning a visual-semantic mapping from seen-class images to class-lev…
Few-Shot Object Detection via Spatial-Channel State Space Model
Zhimeng Xin, Tianxu Wu, Yixiong Zou +3
Due to the limited training samples in few-shot object detection (FSOD), we observe that current methods may struggle to accurately extract effective features from each channel. Sp…
Toward Realistic Camouflaged Object Detection: Benchmarks and Method
Zhimeng Xin, Tianxu Wu, Shiming Chen +5
Camouflaged object detection (COD) primarily relies on semantic or instance segmentation methods. While these methods have made significant advancements in identifying the contours…
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning
Dingjie Fu, Wenjin Hou, Shiming Chen +4
Generative Zero-Shot Learning (ZSL) methods synthesize class-related features based on predefined class semantic prototypes, showcasing superior performance. However, this feature…
Visual-Augmented Dynamic Semantic Prototype for Generative Zero-Shot Learning
Wenjin Hou, Shiming Chen, Shuhuang Chen +6
Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative metho…
Few-Shot Object Detection: Research Advances and Challenges
Zhimeng Xin, Shiming Chen, Tianxu Wu +3
Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such m…
ECEA: Extensible Co-Existing Attention for Few-Shot Object Detection
Zhimeng Xin, Tianxu Wu, Shiming Chen +3
Few-shot object detection (FSOD) identifies objects from extremely few annotated samples. Most existing FSOD methods, recently, apply the two-stage learning paradigm, which transfe…