75 citations · 101 across the 9 of their papers we have counts for
9 papers
MultiCapCLIP: Auto-Encoding Prompts for Zero-Shot Multilingual Visual Captioning
Bang Yang, Fenglin Liu, Xian Wu +3
Supervised visual captioning models typically require a large scale of images or videos paired with descriptions in a specific language (i.e., the vision-caption pairs) for trainin…
Benign Shortcut for Debiasing: Fair Visual Recognition via Intervention with Shortcut Features
Yi Zhang, Jitao Sang, Junyang Wang +2
Machine learning models often learn to make predictions that rely on sensitive social attributes like gender and race, which poses significant fairness risks, especially in societa…
Strip-MLP: Efficient Token Interaction for Vision MLP
Guiping Cao, Shengda Luo, Wenjian Huang +4
Token interaction operation is one of the core modules in MLP-based models to exchange and aggregate information between different spatial locations. However, the power of token in…
Towards Efficient Task-Driven Model Reprogramming with Foundation Models
Shoukai Xu, Jiangchao Yao, Ran Luo +5
Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only s…
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding
Linhui Xiao, Xiaoshan Yang, Fang Peng +3
Visual Grounding (VG) is a crucial topic in the field of vision and language, which involves locating a specific region described by expressions within an image. To reduce the reli…
ZeroNLG: Aligning and Autoencoding Domains for Zero-Shot Multimodal and Multilingual Natural Language Generation
Bang Yang, Fenglin Liu, Yuexian Zou +3
Natural Language Generation (NLG) accepts input data in the form of images, videos, or text and generates corresponding natural language text as output. Existing NLG methods mainly…
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples
Jiaming Zhang, Xingjun Ma, Qi Yi +4
There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noi…
DAS: Densely-Anchored Sampling for Deep Metric Learning
Lizhao Liu, Shangxin Huang, Zhuangwei Zhuang +3
Deep Metric Learning (DML) serves to learn an embedding function to project semantically similar data into nearby embedding space and plays a vital role in many applications, such…
Boost Test-Time Performance with Closed-Loop Inference
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +6
Conventional deep models predict a test sample with a single forward propagation, which, however, may not be sufficient for predicting hard-classified samples. On the contrary, we…