The recent success of deep learning has shown that a deep architecture in conjunction with abundant quantities of labeled training data is the most promising approach for many vision tasks. However, annotating a large-scale dataset for training such deep neural networks is costly and time-consuming, even with the availability of scalable crowdsourcing platforms like Amazon’s Mechanical Turk. As a result, there are relatively few public large-scale datasets (e.g., ImageNet and Places2) from which it is possible to learn generic visual representations from scratch.

Thus, it is unsurprising that there is continued interest in developing novel deep learning systems that trained on low-cost data for image and video recognition tasks. Among different solutions, crawling data from Internet and using the web as a source of supervision for learning deep representations has shown promising performance for a variety of important computer vision applications. However, the datasets and tasks differ in various ways, which makes it difficult to fairly evaluate different solutions, and identify the key issues when learning from web data.

This workshop aims at promoting the advance of learning state-of-the-art visual models directly from the web, and bringing together computer vision researchers in this field. To this end, we release a large scale web image dataset named WebVision or visual understanding by learning from web data. The datasets consists of 16 million of web images crawled from Internet for 5,000 visual concepts. A validation set consists of around 290K images with human annotation will be provided for the convenience of algorithmic development.

Based on this dataset, we also organize the 3rd Challenge on Visual Understanding by Learning from Web Data. The final results will be announced at the workshop, and the winners will be invited to present their approaches at the workshop. An invited paper tack will also be included in the workshop.

==> Result of WebVision 2019 challenge is released. <==

News 10.06.2019: Result of WebVision 2019 is released. Congratulations to all participants! Detailed result of each entry for each team will be provided soon.

News 12.04.2019: An FAQs page is online. A few frequently asked questions regardig the restrctions on data usage are explained in details. Drop us an email if you have other questions.

News 03.04.2019: More benchmark models are being released. Check the github page for updates

News 03.03.2019: A benchmark model based on ResNet-50 is released for reference, which achieves 71.49% top5 accuracy on the validation set. Thank Mr. Qin Wang for producing this benchmark model.

News 26.02.2019: The WebVision 2019 challenge will start on March 1st, 2019.

News 03.01.2019: The workshop website is now online.

Workshop Schedule

Date: June 16th, 2019

Location: Room 203B

Start Time Event
8:30 Opening Remarks
8:40 Invited Talk: Exploring the Limits of Weakly Supervised Pretraining , Dr. Laurens van der Maaten (Facebook AI Research)
9:20 Challenge Overview
10:00 Coffee Break
10:30 Participant Presentation by Alibaba-Vision ( Alibaba Group)
10:50 Participant Presentation by BigVideo (SenseTime)
11:10 Poster Session
  1. Weakly Supervised Deep Image Hashing Through Tag Embeddings, Vijetha R Gattupalli (Arizona State University)*; Yaoxin Zhuo (Arizona State University); baoxin Li (Arizona State University)
  2. Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration, Shuhan Tan (Sun-Yat-Sen University, China)*; Jiening Jiao (Sun Yat-sen University, China ); WEI-SHI ZHENG (Sun Yat-sen University, China)
  3. Large-scale weakly-supervised pre-training for video action recognition, Dhruv Mahajan (Facebook); Deepti Ghadiyaram (Facebook)*; Du Tran (Facebook Research)
  4. Devil is in the Edges: Learning Semantic Boundaries from Noisy Annotations, David Acuna (University of Toronto)*; Amlan Kar (University of Toronto); Sanja Fidler (University of Toronto)
  5. Weakly Supervised Video Moment Retrieval From Text Queries, Niluthpol c Mithun (UC Riverside)*; Sujoy Paul (UC Riverside); Amit Roy-Chowdhury (University of California, Riverside, USA )
  6. Learning to Learn from Noisy Labeled Data, Junnan Li (National University of Singapore)*; Wong Yongkang (National University of Singapore); Qi Zhao (University of Minnesota); Mohan Kankanhalli (National University of Singapore,)
  7. Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search, Abhimanyu Dubey (Massachusetts Institute of Technology)*; Laurens van der Maaten (Facebook); Zeki Yalniz (Facebook); Yixuan Li (Facebook Research); Dhruv Mahajan (Facebook)
  8. Large-scale Long-Tailed Recognition in an Open World, Ziwei Liu (The Chinese University of Hong Kong)*; Zhongqi Miao (UC Berkeley); Xiaohang Zhan (The Chinese University of Hong Kong); Jiayun Wang (UC Berkeley / ICSI); Boqing Gong (Tencent AI Lab); Stella X Yu (UC Berkeley / ICSI)
  9. Large-Scale Few-Shot Learning: Knowledge Transfer with Class Hierarchy, Aoxue Li (Peking University); Tiange Luo (Peking University); Zhiwu Lu (Renmin University of China)*; Tao Xiang (University of Surrey); Liwei Wang (Peking University)
  10. Probabilistic End-to-end Noise Correction for Learning with Noisy Labels, Kun Yi (Nanjing University); Jianxin Wu (Nanjing University)*
  11. The Pros and Cons: Rank-aware Temporal Attention for Skill Determination in Long Videos, Hazel Doughty, Walterio Mayol-Cuevas, Dima Damen
  12. Unsupervised Multi-label Dataset Generation from Web Data, Carlos Roig, David Varas, Issey Masuda, Juan Carlos Riveiro, Elisenda Bou-Balust
  13. Class-incremental Learning via Deep Model Consolidation, Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, C.-C. Jay Kuo
  14. Scandinavian or Mid-century Modern? Cracking Style of Furniture: An E-commerce Perspective, Feng Liu, Min Xie, Alessandro Magnani, Binwei Yang, Sonu Durgia, Somnath Banerjee
Lunch Break
14:00 Invited Talk: Learning about Fashion from Web Photos, Prof. Kristen Grauman (University of Texas at Austin)
14:40 Invited Talk: Deep Learning with Noisy Supervision, Prof. Ivor W.H. Tsang (University of Technology Sydney)
15:20 Participant Presentation by huaweicloud (Futurewei, University of Electronic Science and Technlogy China (UESTC), Huawei Cloud, and Xidian Univesity)
15:40 Award Session & Closing Remarks

Important Dates

Challenge Launch Date March 1, 2019
Challenge Submissions Deadline June 7, 2019
Challenge Award Notification June 10, 2019
Paper Submission Deadline May 15, 2019
Paper Notification May 30, 2019
Workshop date (co-located with CVPR'19) June 16, 2019

All deadlines are at 23:59 Pacific Standard Time.


Prof. Kristen Grauman
Prof. Ivor W.H. Tsang
Laurens van der Maaten


General Chairs

Jesse Berent
Abhinav Gupta
Rahul Sukthankar
Luc Van Gool

Program Chairs

Wen Li
Limin Wang
Wei Li
Eirikur Agustsson