Introduction

Visual recognition is a critical task in computer vision, which has gained significant attention in recent years due to its numerous applications in various fields, including image classification, object detection, semantic segmentation, and instance retrieval for autonomous driving and intelligent surveillance. Over the past decade, deep learning techniques and large-scale datasets have contributed to remarkable advancements in the performance of visual recognition systems. However, existing visual models are often limited by their closed-world assumptions, where all possible classes and domains are known in advance, and all data is given at once. Such assumptions are not applicable in practical scenarios, where novel or previously unseen classes and domains can arise, and data may be continually coming or decentralized due to data privacy concerns. One example of such a scenario is an autonomous vehicle encounters new traffic patterns, a medical AI system detects new diseases, or an intelligent system encounters a criminal wearing a new type of disguise or clothing. Another example is when a healthcare organization collects patient data from multiple hospitals, and the data may be decentralized and continually coming in due to privacy concerns. Moreover, the challenge of multi-modality poses another obstacle, as visual recognition systems must be capable of integrating and handling data from multiple sources, such as images, videos, texts, and 3D models. Therefore, new methods and techniques are needed to address the challenges of open-world visual recognition and enable visual recognition systems to perform effectively in practical scenarios.

Aims&Scope

This special issue invites innovative research papers that aim to address these challenges and propose novel techniques for open-world visual recognition. Potential topics of interest include, but are not limited to:

Novel Class Discovery, where the goal is to discover underlying semantic clusters for unlabeled data including unseen classes

Open-Set Semi-Supervised Learning, where the system should learn from both labeled and unlabeled data, and distinguish between known and unknown classes during testing

Open Vocabulary Visual Learning, where the goal is to train a model that can recognize a broader range of visual concepts, including new and rare ones, using a limited pre-defined set of categories

Out-of-Distribution Detection, where the goal is to distinguish between in-distribution and out-of-distribution samples

Robust/Adversarial Learning, where the goal is to improve the model's robustness to distributional shifts, such as adversarial perturbations and corruptions

Open-World Domain Adaptation, where the goal is to adapt/generalize the model to target domains under open-world scenarios, such as universal, source-free, test-time domain adaptation, and domain generalization

Moreover, this special issue also welcomes papers that focus on:

Developing new techniques for continual learning, federated learning and multi-modality learning in the context of open-world visual recognition

Building datasets and benchmarks for facilitating the study of open-world visual recognition

We encourage submissions that cover a broad range of visual recognition tasks, including but not limited to image classification, object detection, semantic segmentation, action recognition and pose estimation.

Submission Guidelines

Please submit via IJCV Editorial Manager: https://www.editorialmanager.com/visi. Choose SI: Open-World Visual Recognition from the Article Type dropdown.

Submitted papers should present original, unpublished work, relevant to one of the topics of the Special Issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Manuscripts will be subject to a peer reviewing process and must conform to the author guidelines available on the IJCV website at: https://www.springer.com/11263

Important Dates

Notification: Due to ECCV 2024's deadline and high submission volume, decisions of some manuscripts are delayed. We apologize and aim to expedite the process, with final notifications by March 10.

Submission deadline: October 15, 2023 extended to 15 December 2023

First review notification: February 15, 2024

Revised submission deadline: April 15, 2024

Final review notification: May 15, 2024

Final manuscript due: June 15, 2024

Publication date: Spring 2024

Guest Editors

Zhun Zhong: Assistant Professor, University of Trento, Italy, zhunzhong007@gmail.com

Hong Liu, Fellowship Researcher, National Institute of Informatics, Japan, hliu@nii.ac.jp

Yin Cui, Senior Research Scientist, Google, USA, yincui@google.com

Shin'ichi Satoh: Professor, National Institute of Informatics, Japan, satoh@nii.ac.jp

Nicu Sebe: Professor, University of Trento, Italy, niculae.sebe@unitn.it

Ming-Hsuan Yang : Professor, University of California at Merced, USA, mhyang@ucmerced.edu