Topics of interest include but are not limited to:

• Open-set visual recognition methods, including classification, object detection, segmentation in images and videos
• Zero/Few-shot text-to-image generation/editing; Open-domain visual QA & image captioning & Multimodal instruction-following chatbot
• Unified neural networks architectures and training objectives over different CV & MM tasks
• Large-scale pre-training, with images/videos only, image/video-text pairs, and external knoweldge
• Efficient large visual model adaptation methods, measured by #training samples (zero-shot and few-shot), #trainable parameters, throughput, training cost
• New metrics / benchmarks / datasets to evaluate task-level transfer and open-set visual recognition

We accept abstract submissions to our workshop. All submissions shall have maximally 8 pages (excluding references) following the CVPR 2023 author guidelines. All submissions will be reviewed by the Program Committee on the basis of technical quality, relevance to scope of the conference, originality, significance, and clarity. Ther review process is double-blind, and the accepted papers are NOT archived in CVPR proceeding.



Workshop Paper Submission Portal: [CMT]


The two new challenges are developed:

Challenge	Eval Datasets	Eval Metrics	Instructions	Make a Challenge Submission
SGinW	25 Image Segmentation Datasets	Zero, few, full-shot
RF100	100 Object Detection Datasets	Zero, few, full-shot

The two existing challenges associated with this workshop: "Image Classification in the Wild" (ICinW) and "Object Detection in the Wild" (ODinW). We summarize their evaluation datasets and metrics in the table below.



Challenge	Eval Datasets	Eval Metrics	Instructions	Make a Challenge Submission
ICinW	20 Image Classification Datasets	Zero, few, full-shot
ODinW	35 Object Detection Datasets	Zero, few, full-shot

To prevent a race purely in pre-training data and model size, we will have two tracks.
• For the academic track, pre-training data is limited: (1) ICinW: ImageNet21K (Removing ImageNet1K), CC3M+CC12M, YFCC15M; (2) ODinW: Objects365; (3) SGinW: COCO, RefCOCO-g.
• For the industry track, there is no limitation on pre-training data and model size. Teams are required to disclose meta info of model and data if extra data is used. Here are some publicly available image-text datasets: (1) FLAVA Public Multimodal Datasets (PMD) corpus with 70M pairs; (2) LAION with 400M or 5B pairs.

Please see the submission pages for detailed requirements in each Challenge -> Track -> Phase. More information about the challenge benchmark is released: [Benchmark] [Document] [Data Download]. Please reach out if you have any issue in submissions.