Poster "prompt learning" Papers
35 papers found
Conference
Anti-Exposure Bias in Diffusion Models
Junyu Zhang, Daochang Liu, Eunbyung Park et al.
Bayesian Prompt Flow Learning for Zero-Shot Anomaly Detection
Zhen Qu, Xian Tao, Xinyi Gong et al.
Causality-guided Prompt Learning for Vision-language Models via Visual Granulation
Mengyu Gao, Qiulei Dong
Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm
Xue-Feng Zhu, Tianyang Xu, Yifan Pan et al.
Diff-Prompt: Diffusion-driven Prompt Generator with Mask Supervision
Weicai Yan, Wang Lin, Zirun Guo et al.
Distilled Prompt Learning for Incomplete Multimodal Survival Prediction
Yingxue Xu, Fengtao ZHOU, Chenyu Zhao et al.
Exploiting Domain Properties in Language-Driven Domain Generalization for Semantic Segmentation
Seogkyu Jeon, Kibeom Hong, Hyeran Byun
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Xinhua Lu, Runhe Lai, Yanqi Wu et al.
Federated Few-Shot Class-Incremental Learning
Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu et al.
FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts
Weihao Bo, Yanpeng Sun, Yu Wang et al.
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Jiawen Zhu, YEW-SOON ONG, Chunhua Shen et al.
Hierarchical Cross-modal Prompt Learning for Vision-Language Models
Hao Zheng, Shunzhi Yang, Zhuoxin He et al.
Multi-Label Test-Time Adaptation with Bound Entropy Minimization
Xiangyu Wu, Feng Yu, Yang Yang et al.
Multimodal Prompt Alignment for Facial Expression Recognition
Fuyan Ma, Yiran He, Bin Sun et al.
One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning
Wenxi Lv, Qinliang Su, Wenchao Xu
Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
Xiao Guo, Xiufeng Song, Yue Zhang et al.
VaMP: Variational Multi-Modal Prompt Learning for Vision-Language Models
Silin Cheng, Kai Han
Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation
Sua Lee, Kyubum Shin, Jung Ho Park
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt Learning
Chen Jin, Ryutaro Tanno, Amrutha Saseendran et al.
BadCLIP: Trigger-Aware Prompt Learning for Backdoor Attacks on CLIP
Jiawang Bai, Kuofeng Gao, Shaobo Min et al.
GalLop: Learning global and local prompts for vision-language models
Marc Lafon, Elias Ramzi, Clément Rambour et al.
Harnessing Text-to-Image Diffusion Models for Category-Agnostic Pose Estimation
Duo Peng, Zhengbo Zhang, Ping Hu et al.
Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation
Ji-Jia Wu, Andy Chia-Hao Chang, Chieh-Yu Chuang et al.
Improving Zero-Shot Generalization for CLIP with Variational Adapter
Ziqian Lu, Fengli Shen, Mushui Liu et al.
Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation
Marco Mistretta, Alberto Baldrati, Marco Bertini et al.
Large Language Models are Good Prompt Learners for Low-Shot Image Classification
Zhaoheng Zheng, Jingmin Wei, Xuefeng Hu et al.
Learning Transferable Negative Prompts for Out-of-Distribution Detection
Tianqi Li, Guansong Pang, wenjun miao et al.
LiDAR-based All-weather 3D Object Detection via Prompting and Distilling 4D Radar
Yujeong Chae, HYEONSEONG KIM, Changgyoon Oh et al.
MTA-CLIP: Language-Guided Semantic Segmentation with Mask-Text Alignment
Anurag Das, Xinting Hu, Li Jiang et al.
Open-Vocabulary Calibration for Fine-tuned CLIP
Shuoyuan Wang, Jindong Wang, Guoqing Wang et al.
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Xiaofan Li, Zhizhong Zhang, Xin Tan et al.
Robust Calibration of Large Vision-Language Adapters
Balamurali Murugesan, Julio Silva-Rodríguez, Ismail Ben Ayed et al.
Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint
Sixiang Chen, Tian Ye, Kai Zhang et al.
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen et al.
Unknown Prompt the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization
Mainak Singha, Ankit Jha, Shirsha Bose et al.