The WebVision workshop contains a challenge track and an poster track:

Researchers are invited to participate the WebVision challenge, which aims to advance the area of learning useful knowledge and effective representation from noisy web images and meta information. The challenge is based on the WebVision 2018 dataset, which is composed of training, validation, and test set. The training set is downloaded from Web without any human annotation. The validation and test set are human annotated, where the labels of validation data are provided and the ones of test data are withheld. To imitate the setting of learning from web data, the participants are required to learn their models solely on the training set and submit classification results on the test set. In this sense, the validation data and labels could be simply used to tune hyper-parameters and cannot be used to learn the model weights.

The WebVision dataset provides the web images and their corresponding meta information (e.g., query, title, comments, etc.) and more information can be found at the dataset page. Learning from web data poses several challenges such as

1. Label Noise: we can infer pseudo-labels for each instance from web metadata. Such labels are inherently noisy due to inconsistency in the metadata, weak because they typically tag concepts at a coarser granularity than required, and incomplete because they are not reliably present.
2. Better use of meta and cross-modal information: current approaches do not fully exploit either the semantic richness of the available metadata nor do they take advantage of much cross-modal information (e.g., audio and video) present in most web content. Addressing this requires us to consider research issues such as knowledge mining from unstructured data, joint representation learning from both images and language, joint models for audio-visual data, etc.
3. Transfer Learning: the computer vision community recognizes that it is important to generalize the knowledge learnt from one dataset to new domains and new tasks. Therefore, effectively adapting the visual representations learned from the WebVision dataset to new tasks with low sample complexity is a key issue of significant theoretical interest and practical importance.

Participant are encouraged to design new methods to solve these challenges.

A poster session will be held at the workshop. The goal is to provide a stimulating space for researchers to share their works with scientific peers. We welcome researchers to submit their recent works on any topics related to learning from web data.

• Submission to the poster paper track does not require participation in the challenge track.
• The submission can be published or unpublished work, but have to be accessible publicly. We recommend authors to upload their paper on arXiv, but other publicly accessible link is also acceptable.
• There is no requirement on paper format or page limitation. We recommend the CVPR formatting style with 4-8 pages.
• The submission will be reviewed by workshop chairs. Accepted papers will be presented at the poster session at the workshop. Note that all accepted papers will be linked on the workshop website, but will NOT appear in the CVPR workshop proceedings.
• Poster paper are reviewed in a rolling base until the places are fulfilled. Acceptance notification will be sent out once the decision has been made. We encourage people to submit as early as possible. For papers submitted before May 15, 2018, the acceptance notification will be sent out at the latest by May 30, 2018.
• How to submit? For poster paper submission, please send an email titled with "[WebVision2018 Poster Paper Submission] Your Name - Your Paper Title" to webvisionworkshop@gmail.com. The email should contain the following information
• Paper Title
• Author List
• Keywords
• Name of Main Contact
• Email of Main Contact
• Affiliation of Main Contact
• Paper URL
• You can also choose to enclose your submission in the attachment, and we will link it on the workshop website upon acceptance.