9th ABAW

Submission Site

9th Workshop and Competition on


Affective & Behavior Analysis in-the-wild (ABAW)

in conjunction with the International Conference on Computer Vision (ICCV), 2025

8:00 - 12:30 HST, 19 October 2025, Honolulu, Hawaii, USA

About ABAW

The ABAW Workshop is a premier platform highlighting the latest advancements in multimodal analysis, generation, modeling, and understanding of human affect and behavior in real-world, unconstrained environments. It emphasizes cutting-edge systems that integrate facial expressions, body movements, gestures, natural language, voice and speech to enable impactful research and practical applications. The workshop fosters interdisciplinary collaboration across fields such as computer vision, AI, human machine interaction, psychology, robotics, ethics & healthcare. The workshop further addresses complex challenges like algorithmic fairness, demographic bias & data privacy, making it a vital forum for building equitable, generalizable & human-centered AI systems. By uniting experts from academia, industry & government, the workshop promotes innovation, drives knowledge exchange, and inspires new directions in affective computing, behavior modelling and understanding & human-computer interaction. Finally, the Workshop includes a Competition with 3 challenges.

The ABAW Workshop and Competition is a continuation of the respective events held at CVPR 2025, CVPR 2024, 2023, 2022 & 2017, ECCV 2024 & 2022, ICCV 2021, FG 2020 (a) & (b).

Organisers



General Chair



           

Dimitrios Kollias

Queen Mary University of London, UK d.kollias@qmul.ac.uk


Program Chairs



                         

Stefanos Zafeiriou

Imperial College London, UK s.zafeiriou@imperial.ac.uk

Irene Kotsia

Cogitat Ltd, UK irene@cogitat.io

Greg Slabaugh

Queen Mary University of London, UK g.slabaugh@qmul.ac.uk

          Data Chairs

                      Chunchang Shao,                          Queen Mary University of London, UK
                      Guanyu Hu,                                      Queen Mary University of London, UK & Xi'an Jiaotong University, China
                      Damith Chamalke Senadeera,     Queen Mary University of London, UK
                      Kaushal Kumar Keshlal Yadav,     Queen Mary University of London, UK
                      Jianian Zheng,                                  University College London, UK

The Workshop



Call for Papers

Original high-quality contributions, in terms of databases, surveys, studies, foundation models, techniques and methodologies (either uni-modal or multi-modal; uni-task or multi-task ones) are solicited on -but are not limited to- the following topics:

    facial expression (basic, compound or other) or micro-expression analysis

    facial action unit detection

    valence-arousal estimation

    physiological-based (e.g.,EEG, EDA) affect analysis

    face recognition, detection or tracking

    body recognition, detection or tracking

    gesture recognition or detection

    pose estimation or tracking

    activity recognition or tracking

    lip reading and voice understanding

    face and body characterization (e.g., behavioral understanding)

    characteristic analysis (e.g., gait, age, gender, ethnicity recognition)

    group understanding via social cues (e.g., kinship, non-blood relationships, personality)

    video, action and event understanding

    digital human modeling

    characteristic analysis (e.g., gait, age, gender, ethnicity recognition)

    violence detection

    autonomous driving

    domain adaptation, domain generalisation, few- or zero-shot learning for the above cases

    fairness, explainability, interpretability, trustworthiness, privacy-awareness, bias mitigation and/or subgroup distribution shift analysis for the above cases

    editing, manipulation, image-to-image translation, style mixing, interpolation, inversion and semantic diffusion for all afore mentioned cases



Workshop Important Dates


Paper Submission Deadline:                                                             23:59:59 AoE (Anywhere on Earth) July 8, 2025

Review decisions sent to authors; Notification of acceptance:       August 12, 2025

Camera ready version:                                                                       August 18, 2025




Submission Information

The paper format should adhere to the paper submission guidelines for main ICCV 2025 proceedings style. Please have a look at the Submission Guidelines Section here.

We welcome full long paper submissions (between 5 and 8 pages, excluding references or supplementary materials). All submissions must be anonymous and conform to the ICCV 2025 standards for double-blind review.

All papers should be submitted using this CMT website*.

All accepted manuscripts will be part of ICCV 2025 conference proceedings.

At the day of the workshop, oral presentations will be conducted by authors who are attending in-person.



* The Microsoft CMT service was used for managing the peer-reviewing process for this workshop. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.





Workshop Contact Information

For any queries you may have regarding the Workshop, please contact d.kollias@qmul.ac.uk.

The Competition



The Competition is a continuation of the respective Competitions held at CVPR in 2025, 2024, 2023, 2022 & 2017, at ECCV in 2024 & 2022, at ICCV in 2021 and at IEEE FG in 2020. It is split into the three below mentioned Challenges. Participants are invited to participate in at least one of these Challenges.



How to participate

In order to participate, teams will have to register. There is a maximum number of 8 participants in each team.



VA Estimation Challenge

If you want to participate in this Challenge you should follow the below procedure for registration.

The lead researcher should send an email from their official address (no personal emails will be accepted) to d.kollias@qmul.ac.uk with:

i) subject "9th ABAW Competition: Team Registration";

ii) this EULA (if the team is composed of only academics) or this EULA (if the team has at least one member coming from the industry) filled in, signed and attached;

iii) the lead researcher's official academic/industrial website; the lead researcher cannot be a student (UG/PG/Ph.D.);

iv) the emails of each team member, each one in a separate line in the body of the email;

v) the team's name;

vi) the point of contact name and email address (which member of the team will be the main point of contact for future communications, data access etc)

As a reply, you will receive access to the dataset's cropped/cropped-aligned images and annotations and other important information.



CE Recognition Challenge

If you want to participate in this Challenge you should follow the below procedure for registration.

The lead researcher should send an email from their official address (no personal emails will be accepted) to d.kollias@qmul.ac.uk with:

i) subject "9th ABAW Competition: Team Registration";

ii) this EULA (if the team is composed of only academics) or this EULA (if the team has at least one member coming from the industry) filled in, signed and attached;

iii) the lead researcher's official academic/industrial website; the lead researcher cannot be a student (UG/PG/Ph.D.);

iv) the emails of each team member, each one in a separate line in the body of the email;

v) the team's name;

vi) the point of contact name and email address (which member of the team will be the main point of contact for future communications, data access etc)

As a reply, you will receive access to the dataset's videos and other important information.



Fine-Grained VD Challenge

If you want to participate in this Challenge you should follow the below procedure for registration.

The lead researcher should send an email from their official address (no personal emails will be accepted) to d.kollias@qmul.ac.uk with:

i) subject "9th ABAW Competition: Team Registration";

ii) this EULA (if the team is composed of only academics) or this EULA (if the team has at least one member coming from the industry) filled in, signed and attached;

iii) the lead researcher's official academic/industrial website; the lead researcher cannot be a student (UG/PG/Ph.D.);

iv) the emails of each team member, each one in a separate line in the body of the email;

v) the team's name;

vi) the point of contact name and email address (which member of the team will be the main point of contact for future communications, data access etc)

As a reply, you will receive access to the dataset's videos and other important information.



Competition Contact Information

For any queries you may have regarding the Challenges, please contact d.kollias@qmul.ac.uk.


General Information

At the end of the Challenges, each team will have to send us:

i) a link to a Github repository where their solution/source code will be stored,

ii) a link to an ArXiv paper with 4-8 pages describing their proposed methodology, data used and results.

Each team will also need to upload their test set predictions on an evaluation server (details will be circulated when the test set is released).

After that, the winner of each Challenge, along with a leaderboard, will be announced.

There will be one winner per Challenge. The top-3 performing teams of each Challenge will have to contribute paper(s) describing their approach, methodology and results to our Workshop; the accepted papers will be part of the ICCV 2025 proceedings. All other teams are also able to submit paper(s) describing their solutions and final results; the accepted papers will be part of the ICCV 2025 proceedings.

The Competition's white paper (describing the Competition, the data, the baselines and results) will be ready at a later stage and will be distributed to the participating teams.



General Rules

1) Participants can contribute to any of the 3 Challenges.

2) In order to take part in any Challenge, participants will have to register as described above.

3) Any face detector whether commercial or academic can be used in the challenge. The paper accompanying the challenge result submission should contain clear details of the detectors/libraries used.

4) The top performing teams will have to share their solution (code, model weights, executables) with the organisers upon completion of the challenge; in this way the organisers will check so as to prevent cheating or violation of rules.



Competition Important Dates


Call for participation announced, team registration begins, data available:           June 3, 2025

Test set release:                                                                                                               June 27, 2025

Final submission deadline (Predictions, Code and ArXiv paper):                               23:59:59 AoE (Anywhere on Earth) July 3, 2025

Winners Announcement:                                                                                                 July 5, 2025

Final Paper Submission Deadline:                                                                                 23:59:59 AoE (Anywhere on Earth) July 8, 2025

Review decisions sent to authors; Notification of acceptance:                                   August 12, 2025

Camera ready version:                                                                                                   August 18, 2025

Valence-Arousal (VA) Estimation Challenge

Database

For this Challenge, an augmented version of the Aff-Wild2 database will be used. This database is audiovisual (A/V), in-the-wild and in total consists of 594 videos of around 3M frames of 584 subjects annotated in terms of valence and arousal.

Rules

Any solutions (either uni-task or multi-task) will be accepted for this Challenge. Teams are allowed to use any -publicly or not- available pre-trained model (as long as it has not been pre-trained on Aff-Wild2). The pre-trained model can be pre-trained on any task (e.g., VA estimation, Expression Recognition, AU detection, Face Recognition). Teams are allowed to use other databases' annotations, or generated/synthetic data, or any affine transformations, or in general data augmentation techniques (e.g., MixAugment) for increasing the size of the training dataset.

Performance Assessment

The performance measure (P) is the mean Concordance Correlation Coefficient (CCC) of valence and arousal:

CCCarousal + CCCvalence
2

Baseline Results

The baseline network is a pre-trained on ImageNet ResNet-50 and its performance on the validation set is:

CCCvalence = 0.24,     CCCarousal = 0.20

P = 0.22

Compound Expression (CE) Recognition Challenge

Database

For this Challenge, a part of C-EXPR-DB database will be used (56 videos in total). C-EXPR-DB is audiovisual (A/V) in-the-wild database and in total consists of 400 videos of around 200K frames; each frame is annotated in terms of 12 compound expressions. For this Challenge, the following 7 compound expressions will be considered: Fearfully Surprised, Happily Surprised, Sadly Surprised, Disgustedly Surprised, Angrily Surprised, Sadly Fearful and Sadly Angry.

Goal of the Challenge and Rules

Participants will be provided with a part of C-EXPR-DB database (56 videos in total), which will be unannotated, and will be required to develop their methodologies (supervised/self-supervised, domain adaptation, zero-/few-shot learning etc) for recognising the 7 compound expressions in this unannotated part, in a per-frame basis.

Teams are allowed to use any -publicly or not- available pre-trained model and any -publicly or not- available database (that contains any annotations, e.g. VA, basic or compound expressions, AUs)

Performance Assessment

The performance measure (P) is the average F1 Score across all 7 categories:   ∑ F1/7

Fine-Grained Violence Detection (VD) Challenge

Database

For this Challenge, a part of DVD database will be used. DVD database is a large-scale (over 500 videos, 2.7M frames), frame-level annotated VD database with diverse environments, varying lighting conditions, multiple camera sources, complex social interactions, and rich metadata. DVD is designed to capture the complexities of real-world violent events.

Goal of the Challenge and Rules

Participants will be provided with a subset of the DVD Database and will be tasked with developing AI, machine learning, or deep learning models for fine-grained violence detection (VD), specifically at the frame level. Each frame in the DVD Database is annotated as either violent or non-violent. Participants are required to predict, for every frame, whether it depicts a violent event (label: 1) or a non-violent event (label: 0).

Teams are allowed to use any -publicly or not- available pre-trained model and any -publicly or not- available database.

Performance Assessment

The performance measure (P) is the macro F1 Score across the two categories:   ∑ F1/2

Baseline Results

The baseline network is a pre-trained on ImageNet ResNet-50 and its performance on the validation set is:

P = 0.73

References


If you use the above data, you must cite all following papers:

    D. Kollias, et. al.: "Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition", 2025

    @article{Kollias2025, author = "Dimitrios Kollias and Panagiotis Tzirakis and Alan S. Cowen and Stefanos Zafeiriou and Irene Kotsia and Eric Granger and Marco Pedersoli and Simon L. Bacon and Alice Baird and Chris Gagne and Chunchang Shao and Guanyu Hu and Soufiane Belharbi and Muhammad Haseeb Aslam", title = "{Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition}", year = "2025", month = "3", url = "https://figshare.com/articles/preprint/CVPR_2025_ABAW8_baseline_paper_arxiv_pdf/28524563", doi = "10.6084/m9.figshare.28524563.v4"}

    @article{kolliasadvancements, title={Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition}, author={Kollias, Dimitrios and Tzirakis, Panagiotis and Cowen, Alan and Kotsia, Irene and Cogitat, UK and Granger, Eric and Pedersoli, Marco and Bacon, Simon and Baird, Alice and Shao, Chunchang and others}}

    D. Kollias, et. al.: "DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios", 2025

    @misc{kollias2025dvd, author = {Kollias, Dimitrios and Senadeera, Damith and Zheng, Jianian and Yadav, Kaushal and Slabaugh, Greg and Awais, Muhammad and Yang, Xiaoyun}, title = {DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios}, year = {2025}, howpublished = {\url{https://www.researchgate.net/publication/392397877_DVD_A_Comprehensive_Dataset_for_Advancing_Violence_D etection_in_Real-World_Scenarios}}, note = {DOI: \href{https://doi.org/10.13140/RG.2.2.31957.33762}{10.13140/RG.2.2.31957.33762}}}

    D. Kollias, et. al.: "7th abaw competition: Multi-task learning and compound expression recognition", 2024

    @article{kollias20247th,title={7th abaw competition: Multi-task learning and compound expression recognition},author={Kollias, Dimitrios and Zafeiriou, Stefanos and Kotsia, Irene and Dhall, Abhinav and Ghosh, Shreya and Shao, Chunchang and Hu, Guanyu},journal={arXiv preprint arXiv:2407.03835},year={2024}}

    D. Kollias, et. al.: "The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition". IEEE CVPR, 2024

    @inproceedings{kollias20246th,title={The 6th affective behavior analysis in-the-wild (abaw) competition},author={Kollias, Dimitrios and Tzirakis, Panagiotis and Cowen, Alan and Zafeiriou, Stefanos and Kotsia, Irene and Baird, Alice and Gagne, Chris and Shao, Chunchang and Hu, Guanyu},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={4587--4598},year={2024}}

    D. Kollias, et. al.: "Distribution matching for multi-task learning of classification tasks: a large-scale study on faces & beyond". AAAI, 2024

    @inproceedings{kollias2024distribution,title={Distribution matching for multi-task learning of classification tasks: a large-scale study on faces \& beyond},author={Kollias, Dimitrios and Sharmanska, Viktoriia and Zafeiriou, Stefanos},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},volume={38},number={3},pages={2813--2821},year={2024}}

    D. Kollias, et. al.: "ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Emotional Reaction Intensity Estimation Challenges". IEEE CVPR, 2023

    @inproceedings{kollias2023abaw2, title={Abaw: Valence-arousal estimation, expression recognition, action unit detection \& emotional reaction intensity estimation challenges}, author={Kollias, Dimitrios and Tzirakis, Panagiotis and Baird, Alice and Cowen, Alan and Zafeiriou, Stefanos}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={5888--5897}, year={2023}}

    D. Kollias: "Multi-Label Compound Expression Recognition: C-EXPR Database & Network". IEEE CVPR, 2023

    @inproceedings{kollias2023multi, title={Multi-Label Compound Expression Recognition: C-EXPR Database \& Network}, author={Kollias, Dimitrios}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={5589--5598}, year={2023}}

    D. Kollias: "ABAW: Learning from Synthetic Data & Multi-Task Learning Challenges". ECCV, 2022

    @inproceedings{kollias2023abaw, title={ABAW: learning from synthetic data \& multi-task learning challenges}, author={Kollias, Dimitrios}, booktitle={European Conference on Computer Vision}, pages={157--172}, year={2023}, organization={Springer} }

    D. Kollias: "ABAW: Valence-Arousal Estimation, Expression Recognition, Action Unit Detection & Multi-Task Learning Challenges". IEEE CVPR, 2022

    @inproceedings{kollias2022abaw, title={Abaw: Valence-arousal estimation, expression recognition, action unit detection \& multi-task learning challenges}, author={Kollias, Dimitrios}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages={2328--2336}, year={2022} }

    D. Kollias, et. al.: "Analysing Affective Behavior in the second ABAW2 Competition". ICCV, 2021

    @inproceedings{kollias2021analysing, title={Analysing affective behavior in the second abaw2 competition}, author={Kollias, Dimitrios and Zafeiriou, Stefanos}, booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, pages={3652--3660}, year={2021}}

    D. Kollias,S. Zafeiriou: "Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework, 2021

    @article{kollias2021affect, title={Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework}, author={Kollias, Dimitrios and Zafeiriou, Stefanos}, journal={arXiv preprint arXiv:2103.15792}, year={2021}}

    D. Kollias, et. al.: "Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study", 2021

    @article{kollias2021distribution, title={Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study}, author={Kollias, Dimitrios and Sharmanska, Viktoriia and Zafeiriou, Stefanos}, journal={arXiv preprint arXiv:2105.03790}, year={2021} }

    D. Kollias, et. al.: "Analysing Affective Behavior in the First ABAW 2020 Competition". IEEE FG, 2020

    @inproceedings{kollias2020analysing, title={Analysing Affective Behavior in the First ABAW 2020 Competition}, author={Kollias, D and Schulc, A and Hajiyev, E and Zafeiriou, S}, booktitle={2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)(FG)}, pages={794--800}}

    D. Kollias, S. Zafeiriou: "Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace". BMVC, 2019

    @article{kollias2019expression, title={Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace}, author={Kollias, Dimitrios and Zafeiriou, Stefanos}, journal={arXiv preprint arXiv:1910.04855}, year={2019}}

    D. Kollias, et. al.: "Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond". International Journal of Computer Vision (IJCV), 2019

    @article{kollias2019deep, title={Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond}, author={Kollias, Dimitrios and Tzirakis, Panagiotis and Nicolaou, Mihalis A and Papaioannou, Athanasios and Zhao, Guoying and Schuller, Bj{\"o}rn and Kotsia, Irene and Zafeiriou, Stefanos}, journal={International Journal of Computer Vision}, pages={1--23}, year={2019}, publisher={Springer} }

    D. Kollias, et at.: "Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network", 2019

    @article{kollias2019face,title={Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network}, author={Kollias, Dimitrios and Sharmanska, Viktoriia and Zafeiriou, Stefanos}, journal={arXiv preprint arXiv:1910.11111}, year={2019}}

    S. Zafeiriou, et. al. "Aff-Wild: Valence and Arousal in-the-wild Challenge". IEEE CVPR, 2017

    @inproceedings{zafeiriou2017aff, title={Aff-wild: Valence and arousal ‘in-the-wild’challenge}, author={Zafeiriou, Stefanos and Kollias, Dimitrios and Nicolaou, Mihalis A and Papaioannou, Athanasios and Zhao, Guoying and Kotsia, Irene}, booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on}, pages={1980--1987}, year={2017}, organization={IEEE} }

Sponsors


The Affective Behavior Analysis in-the-wild Workshop and Competition has been generously supported by:

    Queen Mary University of London

    QMUL

    Imperial College London

    ICL

    Hume AI

    HUME

    École de technologie supérieure

    ETS

    Concordia University

    CON