网站建设制作设计公司,公司logo墙设计图片,wordpress手机中文版,定制网站模板站文章目录 CVPR2023一. Vision and Language / Multimodal CVPR2023
根据官方信息统计#xff0c;今年共收到 9155 份提交#xff0c;比去年增加了 12%#xff0c;创下新纪录#xff0c;今年接收了 2360 篇论文#xff0c;接收率为 25.78%。作为对比#xff0c;去年有 81… 文章目录 CVPR2023一. Vision and Language / Multimodal CVPR2023
根据官方信息统计今年共收到 9155 份提交比去年增加了 12%创下新纪录今年接收了 2360 篇论文接收率为 25.78%。作为对比去年有 8100 多篇有效投稿大会接收了 2067 篇接收率为 25%。
https://cvpr2023.thecvf.com/Conferences/2023/AcceptedPapers
现在根据关键词对自己感兴趣的方向进行规整以及分类有筛选
一. Vision and Language / Multimodal
论文名简介Improving Commonsense in Vision-Language Models via Knowledge Graph RiddlesFiltering, Distillation, and Hard Negatives for Vision-Language Pre-TrainingSeeing What You Miss: Vision-Language Pre-training with Semantic Completion LearningUni-Perceiver v2: A Generalist Model for Large-Scale Vision and Vision-Language TasksCREPE: Can Vision-Language Foundation Models Reason Compositionally?Task Residual for Tuning Vision-Language ModelsQ: How to Specialize Large Vision-Language Models to Data-Scarce VQA Tasks? A Self-Train on Unlabeled Images!FAME-ViL: Multi-Tasking Vision-Language Model for Heterogeneous Fashion TasksVILA: Learning Image Aesthetics from User Comments with Vision-Language PretrainingOpen-set Fine-grained Retrieval via Prompting Vision-Language EvaluatorImage as a Foreign Language BEiT Pretraining for Vision and Vision-Language TasksFashionSAP: Symbols and Attributes Prompt for Fine-grained Fashion Vision-Language Pre-trainingAccelerating Vision-Language Pretraining with Free Language ModelingLeveraging per Image-Token Consistency for Vision-Language Pre-trainingPosition-guided Text Prompt for Vision-Language Pre-trainingIFSeg: Image-free Semantic Segmentation via Vision-Language ModelEnhanced Multimodal Representation Learning with Cross-modal KDEfficient Multimodal Fusion via Interactive PromptingBest of Both Worlds: Multimodal Contrastive Learning with Tabular and Imaging DataRevisiting Multimodal Representation in Contrastive Learning From Patch and Token embeddings to Finite Discrete TokensAlign and Attend: Multimodal Summarization with Dual Contrastive LossesMultimodal Prompting with Missing Modalities for Visual Recognition
