---
OA_place: repository
OA_type: green
_id: '21070'
abstract:
- lang: eng
  text: 'Deep learning systems deployed in real-world applications often encounter
    data that is different from their in-distribution (ID). A reliable model should
    ideally abstain from making decisions in this out-of-distribution (OOD) setting.
    Existing state-of-the-art methods primarily focus on feature distances, such as
    k-th nearest neighbors and distances to decision boundaries, either overlooking
    or ineffectively using in-distribution statistics. In this work, we propose a
    novel angle-based metric for OOD detection that is computed relative to the in-distribution
    structure. We demonstrate that the angles between feature representations and
    decision boundaries, viewed from the mean of in-distribution features, serve as
    an effective discriminative factor between ID and OOD data. We evaluate our method
    on nine ImageNet-pretrained models. Our approach achieves the lowest FPR in 5
    out of 9 ImageNet models, obtains the best average FPR overall, and consistently
    ranking among the top 3 across all evaluated models. Furthermore, we highlight
    the benefits of contrastive representations by showing strong performance with
    ResNet SCL and CLIP architectures. Finally, we demonstrate that the scale-invariant
    nature of our score enables an ensemble strategy via simple score summation. '
acknowledgement: "This research was funded in whole or in part by the Austrian Science
  Fund (FWF) 10.55776/COE12. For open access purposes, the author has applied a CC
  BY public copyright license to any accepted manuscript version arising from this
  submission.\r\n"
alternative_title:
- Advances in Neural Information Processing Systems
article_processing_charge: No
arxiv: 1
author:
- first_name: Berker
  full_name: Demirel, Berker
  id: 8b4bc47f-3200-11ee-973b-8f0e7be21a9f
  last_name: Demirel
- first_name: 'Marco '
  full_name: 'Fumero, Marco '
  last_name: Fumero
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
citation:
  ama: 'Demirel B, Fumero M, Locatello F. Out-of-Distribution detection with relative
    angles. In: <i>39th Annual Conference on Neural Information Processing Systems</i>.
    Vol 38. Neural Information Processing Systems Foundation; 2025.'
  apa: 'Demirel, B., Fumero, M., &#38; Locatello, F. (2025). Out-of-Distribution detection
    with relative angles. In <i>39th Annual Conference on Neural Information Processing
    Systems</i> (Vol. 38). San Diego, CA, United States: Neural Information Processing
    Systems Foundation.'
  chicago: Demirel, Berker, Marco  Fumero, and Francesco Locatello. “Out-of-Distribution
    Detection with Relative Angles.” In <i>39th Annual Conference on Neural Information
    Processing Systems</i>, Vol. 38. Neural Information Processing Systems Foundation,
    2025.
  ieee: B. Demirel, M. Fumero, and F. Locatello, “Out-of-Distribution detection with
    relative angles,” in <i>39th Annual Conference on Neural Information Processing
    Systems</i>, San Diego, CA, United States, 2025, vol. 38.
  ista: 'Demirel B, Fumero M, Locatello F. 2025. Out-of-Distribution detection with
    relative angles. 39th Annual Conference on Neural Information Processing Systems.
    NeurIPS: Neural Information Processing Systems, Advances in Neural Information
    Processing Systems, vol. 38.'
  mla: Demirel, Berker, et al. “Out-of-Distribution Detection with Relative Angles.”
    <i>39th Annual Conference on Neural Information Processing Systems</i>, vol. 38,
    Neural Information Processing Systems Foundation, 2025.
  short: B. Demirel, M. Fumero, F. Locatello, in:, 39th Annual Conference on Neural
    Information Processing Systems, Neural Information Processing Systems Foundation,
    2025.
conference:
  end_date: 2025-12-07
  location: San Diego, CA, United States
  name: 'NeurIPS: Neural Information Processing Systems'
  start_date: 2025-12-02
corr_author: '1'
date_created: 2026-01-29T14:26:47Z
date_published: 2025-12-01T00:00:00Z
date_updated: 2026-02-16T11:38:25Z
day: '01'
ddc:
- '000'
department:
- _id: FrLo
external_id:
  arxiv:
  - '2410.04525'
has_accepted_license: '1'
intvolume: '        38'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2410.04525
month: '12'
oa: 1
oa_version: Preprint
publication: 39th Annual Conference on Neural Information Processing Systems
publication_identifier:
  issn:
  - 1049-5258
publication_status: epub_ahead
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/berkerdemirel/ORA-OOD-Detection-with-Relative-Angles
status: public
title: Out-of-Distribution detection with relative angles
tmp:
  image: /images/cc_by.png
  legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
  name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
  short: CC BY (4.0)
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 38
year: '2025'
...
---
OA_place: repository
OA_type: green
_id: '18956'
abstract:
- lang: eng
  text: 'Group Activity Recognition (GAR) aims to detect the activity performed by
    multiple actors in a scene. Prior works model the spatio-temporal features based
    on the RGB, optical flow or keypoint data types. On the contrary, our hypothesis
    is that by only using the RGB data without temporality, the performance can be
    maintained with a negligible loss in accuracy. To that end, we propose a novel
    GAR technique for volleyball videos, DECOMPL, which consists of two complementary
    branches. In the visual branch, it extracts the features using attention pooling.
    In the coordinate branch, it considers the configuration of the players and extracts
    the spatial information from the box coordinates. Moreover, we analyzed the Volleyball
    dataset that the recent literature is mostly based on, and systematically reannotated
    it to emphasize the group concept. Experimental results demonstrated the effectiveness
    of the proposed model DECOMPL, which delivered the best/second best GAR performance
    with the reannotations/original annotations among the comparable state-of-the-art
    methods. Code and new annotations are available at GitHub: https://github.com/berkerdemirel/decompl'
article_processing_charge: No
arxiv: 1
author:
- first_name: Berker
  full_name: Demirel, Berker
  id: 8b4bc47f-3200-11ee-973b-8f0e7be21a9f
  last_name: Demirel
- first_name: Huseyin
  full_name: Ozkan, Huseyin
  last_name: Ozkan
citation:
  ama: 'Demirel B, Ozkan H. Decompl: Decompositional learning with attention pooling
    for group activity recognition from a single volleyball image. In: <i>2024 IEEE
    International Conference on Image Processing</i>. IEEE; 2024:977-983. doi:<a href="https://doi.org/10.1109/icip51287.2024.10647499">10.1109/icip51287.2024.10647499</a>'
  apa: 'Demirel, B., &#38; Ozkan, H. (2024). Decompl: Decompositional learning with
    attention pooling for group activity recognition from a single volleyball image.
    In <i>2024 IEEE International Conference on Image Processing</i> (pp. 977–983).
    Abu Dhabi, United Arab Emirates: IEEE. <a href="https://doi.org/10.1109/icip51287.2024.10647499">https://doi.org/10.1109/icip51287.2024.10647499</a>'
  chicago: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning
    with Attention Pooling for Group Activity Recognition from a Single Volleyball
    Image.” In <i>2024 IEEE International Conference on Image Processing</i>, 977–83.
    IEEE, 2024. <a href="https://doi.org/10.1109/icip51287.2024.10647499">https://doi.org/10.1109/icip51287.2024.10647499</a>.'
  ieee: 'B. Demirel and H. Ozkan, “Decompl: Decompositional learning with attention
    pooling for group activity recognition from a single volleyball image,” in <i>2024
    IEEE International Conference on Image Processing</i>, Abu Dhabi, United Arab
    Emirates, 2024, pp. 977–983.'
  ista: 'Demirel B, Ozkan H. 2024. Decompl: Decompositional learning with attention
    pooling for group activity recognition from a single volleyball image. 2024 IEEE
    International Conference on Image Processing. ICIP: International Conference on
    Image Processing, 977–983.'
  mla: 'Demirel, Berker, and Huseyin Ozkan. “Decompl: Decompositional Learning with
    Attention Pooling for Group Activity Recognition from a Single Volleyball Image.”
    <i>2024 IEEE International Conference on Image Processing</i>, IEEE, 2024, pp.
    977–83, doi:<a href="https://doi.org/10.1109/icip51287.2024.10647499">10.1109/icip51287.2024.10647499</a>.'
  short: B. Demirel, H. Ozkan, in:, 2024 IEEE International Conference on Image Processing,
    IEEE, 2024, pp. 977–983.
conference:
  end_date: 2024-10-30
  location: Abu Dhabi, United Arab Emirates
  name: 'ICIP: International Conference on Image Processing'
  start_date: 2024-10-27
corr_author: '1'
date_created: 2025-01-29T12:22:24Z
date_published: 2024-11-01T00:00:00Z
date_updated: 2025-09-09T12:13:12Z
day: '01'
department:
- _id: FrLo
doi: 10.1109/icip51287.2024.10647499
external_id:
  arxiv:
  - '2303.06439'
  isi:
  - '001442947000143'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2303.06439
month: '11'
oa: 1
oa_version: Preprint
page: 977-983
publication: 2024 IEEE International Conference on Image Processing
publication_identifier:
  eisbn:
  - '9798350349399'
  eissn:
  - 2381-8549
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
  link:
  - relation: software
    url: https://github.com/berkerdemirel/decompl
status: public
title: 'Decompl: Decompositional learning with attention pooling for group activity
  recognition from a single volleyball image'
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2024'
...