---
_id: '10882'
abstract:
- lang: eng
  text: 'We introduce Intelligent Annotation Dialogs for bounding box annotation.
    We train an agent to automatically choose a sequence of actions for a human annotator
    to produce a bounding box in a minimal amount of time. Specifically, we consider
    two actions: box verification [34], where the annotator verifies a box generated
    by an object detector, and manual box drawing. We explore two kinds of agents,
    one based on predicting the probability that a box will be positively verified,
    and the other based on reinforcement learning. We demonstrate that (1) our agents
    are able to learn efficient annotation strategies in several scenarios, automatically
    adapting to the image difficulty, the desired quality of the boxes, and the detector
    strength; (2) in all scenarios the resulting annotation dialogs speed up annotation
    compared to manual box drawing alone and box verification alone, while also outperforming
    any fixed combination of verification and drawing in most scenarios; (3) in a
    realistic scenario where the detector is iteratively re-trained, our agents evolve
    a series of strategies that reflect the shifting trade-off between verification
    and drawing as the detector grows stronger.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Jasper
  full_name: Uijlings, Jasper
  last_name: Uijlings
- first_name: Ksenia
  full_name: Konyushkova, Ksenia
  last_name: Konyushkova
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
- first_name: Vittorio
  full_name: Ferrari, Vittorio
  last_name: Ferrari
citation:
  ama: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs
    for bounding box annotation. In: <i>2018 IEEE/CVF Conference on Computer Vision
    and Pattern Recognition</i>. IEEE; 2018:9175-9184. doi:<a href="https://doi.org/10.1109/cvpr.2018.00956">10.1109/cvpr.2018.00956</a>'
  apa: 'Uijlings, J., Konyushkova, K., Lampert, C., &#38; Ferrari, V. (2018). Learning
    intelligent dialogs for bounding box annotation. In <i>2018 IEEE/CVF Conference
    on Computer Vision and Pattern Recognition</i> (pp. 9175–9184). Salt Lake City,
    UT, United States: IEEE. <a href="https://doi.org/10.1109/cvpr.2018.00956">https://doi.org/10.1109/cvpr.2018.00956</a>'
  chicago: Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari.
    “Learning Intelligent Dialogs for Bounding Box Annotation.” In <i>2018 IEEE/CVF
    Conference on Computer Vision and Pattern Recognition</i>, 9175–84. IEEE, 2018.
    <a href="https://doi.org/10.1109/cvpr.2018.00956">https://doi.org/10.1109/cvpr.2018.00956</a>.
  ieee: J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent
    dialogs for bounding box annotation,” in <i>2018 IEEE/CVF Conference on Computer
    Vision and Pattern Recognition</i>, Salt Lake City, UT, United States, 2018, pp.
    9175–9184.
  ista: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent
    dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision
    and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition,
    9175–9184.'
  mla: Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”
    <i>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, IEEE,
    2018, pp. 9175–84, doi:<a href="https://doi.org/10.1109/cvpr.2018.00956">10.1109/cvpr.2018.00956</a>.
  short: J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference
    on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
conference:
  end_date: 2018-06-23
  location: Salt Lake City, UT, United States
  name: 'CVF: Conference on Computer Vision and Pattern Recognition'
  start_date: 2018-06-18
date_created: 2022-03-18T12:45:09Z
date_published: 2018-12-17T00:00:00Z
date_updated: 2023-09-19T15:11:49Z
day: '17'
department:
- _id: ChLa
doi: 10.1109/cvpr.2018.00956
external_id:
  arxiv:
  - '1712.08087'
  isi:
  - '000457843609036'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: ' https://doi.org/10.48550/arXiv.1712.08087'
month: '12'
oa: 1
oa_version: Preprint
page: 9175-9184
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
  eissn:
  - 2575-7075
  isbn:
  - '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning intelligent dialogs for bounding box annotation
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '273'
abstract:
- lang: eng
  text: The accuracy of information retrieval systems is often measured using complex
    loss functions such as the average precision (AP) or the normalized discounted
    cumulative gain (NDCG). Given a set of positive and negative samples, the parameters
    of a retrieval system can be estimated by minimizing these loss functions. However,
    the non-differentiability and non-decomposability of these loss functions does
    not allow for simple gradient based optimization algorithms. This issue is generally
    circumvented by either optimizing a structured hinge-loss upper bound to the loss
    function or by using asymptotic methods like the direct-loss minimization framework.
    Yet, the high computational complexity of loss-augmented inference, which is necessary
    for both the frameworks, prohibits its use in large training data sets. To alleviate
    this deficiency, we present a novel quicksort flavored algorithm for a large class
    of non-decomposable loss functions. We provide a complete characterization of
    the loss functions that are amenable to our algorithm, and show that it includes
    both AP and NDCG based loss functions. Furthermore, we prove that no comparison
    based algorithm can improve upon the computational complexity of our approach
    asymptotically. We demonstrate the effectiveness of our approach in the context
    of optimizing the structured hinge loss upper bound of AP and NDCG loss for learning
    models for a variety of vision tasks. We show that our approach provides significantly
    better results than simpler decomposable loss functions, while requiring a comparable
    training time.
article_processing_charge: No
arxiv: 1
author:
- first_name: Pritish
  full_name: Mohapatra, Pritish
  last_name: Mohapatra
- first_name: Michal
  full_name: Rolinek, Michal
  id: 3CB3BC06-F248-11E8-B48F-1D18A9856A87
  last_name: Rolinek
- first_name: C V
  full_name: Jawahar, C V
  last_name: Jawahar
- first_name: Vladimir
  full_name: Kolmogorov, Vladimir
  id: 3D50B0BA-F248-11E8-B48F-1D18A9856A87
  last_name: Kolmogorov
- first_name: M Pawan
  full_name: Kumar, M Pawan
  last_name: Kumar
citation:
  ama: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. Efficient optimization
    for rank-based loss functions. In: <i>2018 IEEE/CVF Conference on Computer Vision
    and Pattern Recognition</i>. IEEE; 2018:3693-3701. doi:<a href="https://doi.org/10.1109/cvpr.2018.00389">10.1109/cvpr.2018.00389</a>'
  apa: 'Mohapatra, P., Rolinek, M., Jawahar, C. V., Kolmogorov, V., &#38; Kumar, M.
    P. (2018). Efficient optimization for rank-based loss functions. In <i>2018 IEEE/CVF
    Conference on Computer Vision and Pattern Recognition</i> (pp. 3693–3701). Salt
    Lake City, UT, USA: IEEE. <a href="https://doi.org/10.1109/cvpr.2018.00389">https://doi.org/10.1109/cvpr.2018.00389</a>'
  chicago: Mohapatra, Pritish, Michal Rolinek, C V Jawahar, Vladimir Kolmogorov, and
    M Pawan Kumar. “Efficient Optimization for Rank-Based Loss Functions.” In <i>2018
    IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, 3693–3701.
    IEEE, 2018. <a href="https://doi.org/10.1109/cvpr.2018.00389">https://doi.org/10.1109/cvpr.2018.00389</a>.
  ieee: P. Mohapatra, M. Rolinek, C. V. Jawahar, V. Kolmogorov, and M. P. Kumar, “Efficient
    optimization for rank-based loss functions,” in <i>2018 IEEE/CVF Conference on
    Computer Vision and Pattern Recognition</i>, Salt Lake City, UT, USA, 2018, pp.
    3693–3701.
  ista: 'Mohapatra P, Rolinek M, Jawahar CV, Kolmogorov V, Kumar MP. 2018. Efficient
    optimization for rank-based loss functions. 2018 IEEE/CVF Conference on Computer
    Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern
    Recognition, 3693–3701.'
  mla: Mohapatra, Pritish, et al. “Efficient Optimization for Rank-Based Loss Functions.”
    <i>2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition</i>, IEEE,
    2018, pp. 3693–701, doi:<a href="https://doi.org/10.1109/cvpr.2018.00389">10.1109/cvpr.2018.00389</a>.
  short: P. Mohapatra, M. Rolinek, C.V. Jawahar, V. Kolmogorov, M.P. Kumar, in:, 2018
    IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp.
    3693–3701.
conference:
  end_date: 2018-06-22
  location: Salt Lake City, UT, USA
  name: 'CVPR: Conference on Computer Vision and Pattern Recognition'
  start_date: 2018-06-18
date_created: 2018-12-11T11:45:33Z
date_published: 2018-06-28T00:00:00Z
date_updated: 2023-09-11T13:24:43Z
day: '28'
department:
- _id: VlKo
doi: 10.1109/cvpr.2018.00389
ec_funded: 1
external_id:
  arxiv:
  - '1604.08269'
  isi:
  - '000457843603087'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1604.08269
month: '06'
oa: 1
oa_version: Preprint
page: 3693-3701
project:
- _id: 25FBA906-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '616160'
  name: 'Discrete Optimization in Computer Vision: Theory and Practice'
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
  isbn:
  - '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Efficient optimization for rank-based loss functions
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...