The 2nd Workshop on Computer Vision in the Wild
@ CVPR 2023, June 19

Overview

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concepts.

Recent works show that learning from large-scale image-text data is a promising approach to building transferable visual models that can effortlessly adapt to a wide range of downstream computer vision (CV) and multimodal (MM) tasks. For example, CLIP, ALIGN and Florence for image classification, ViLD, RegionCLIP, GLIP and OWL-ViT for object detection, GroupViT, OpenSeg, MaskCLIP and X-Decoder for segmentation. These vision models with language interface are naturally open-vocabulary recogntion models, showing superior zero-shot and few-shot adaption performance on various real-world scenarios.

We host this "Computer Vision in the Wild (CVinW)" workshop, aiming to gather academic and industry communities to work on CV and MM problems in real-world scenarios, focusing on the challenge of open-set/domain visual recognition at different granularities and efficient task-level transfer. To measure the progress of CVinW, we develop new benchmarks for image classification, object detection and segmentation to measure the task-level transfer ablity of various models/methods over diverse real-world datasets, in terms of both prediction accuracy and adaption efficiency. This workshop is a continuation of our ECCV 2022 CVinW Workshop. For those who are new to this topic, please check out the CVinW Reading List.

Segmentation in the Wild (SGinW): Open-set instance/semantic/panoptic segmentation on dozens of semgnetaion datasets in the realistic scenarios.
Roboflow 100 for Object Detection in the Wild: An augmented version of our ODinW by increasing the datasets to hundreds to cover more diverse application domains.

ELEVATER benchmark

Image Classification in the Wild (ICinW)
Object Detection in the Wild (ODinW)

Dates

Feb, 2023 Competition starts, testing phase begins June 2nd, 2023 Competition ends (challenge paper submission) April 21st, 2023 Workshop paper submission deadline May 19th, 2023 Workshop paper acceptance decision to authors June 2nd, 2023 Camera-ready submission deadline

Invited Speakers/Panelists

Kristen Grauman
University of Texas at Austin

Boqing Gong
Google

Justin Johnson
University of Michigan | FAIR

Yinfei Yang
Apple

Bryan A. Plummer
Boston University

Ziwei Liu
NTU

Jacob Solawetz
Roboflow

Anelia Angelova
Google Brain

Jiasen Lu
Allen Instutite for AI

Katerina Fragkiadaki
CMU

Dhruv Batra
Georgia Tech | FAIR

Call for Papers

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
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

Workshop Paper Submission Portal: [CMT]

Computer Vision in the Wild Challenges

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

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

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.