---
_id: '7936'
abstract:
- lang: eng
  text: 'State-of-the-art detection systems are generally evaluated on their ability
    to exhaustively retrieve objects densely distributed in the image, across a wide
    variety of appearances and semantic categories. Orthogonal to this, many real-life
    object detection applications, for example in remote sensing, instead require
    dealing with large images that contain only a few small objects of a single class,
    scattered heterogeneously across the space. In addition, they are often subject
    to strict computational constraints, such as limited battery capacity and computing
    power.To tackle these more practical scenarios, we propose a novel flexible detection
    scheme that efficiently adapts to variable object sizes and densities: We rely
    on a sequence of detection stages, each of which has the ability to predict groups
    of objects as well as individuals. Similar to a detection cascade, this multi-stage
    architecture spares computational effort by discarding large irrelevant regions
    of the image early during the detection process. The ability to group objects
    provides further computational and memory savings, as it allows working with lower
    image resolutions in early stages, where groups are more easily detected than
    individuals, as they are more salient. We report experimental results on two aerial
    image datasets, and show that the proposed method is as accurate yet computationally
    more efficient than standard single-shot detectors, consistently across three
    different backbone architectures.'
article_number: 1716-1725
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
  full_name: Royer, Amélie
  id: 3811D890-F248-11E8-B48F-1D18A9856A87
  last_name: Royer
  orcid: 0000-0002-8407-0705
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in
    low-resource scenarios. In: <i>IEEE Winter Conference on Applications of Computer
    Vision</i>. IEEE; 2020. doi:<a href="https://doi.org/10.1109/WACV45572.2020.9093288">10.1109/WACV45572.2020.9093288</a>'
  apa: 'Royer, A., &#38; Lampert, C. (2020). Localizing grouped instances for efficient
    detection in low-resource scenarios. In <i>IEEE Winter Conference on Applications
    of Computer Vision</i>.  Snowmass Village, CO, United States: IEEE. <a href="https://doi.org/10.1109/WACV45572.2020.9093288">https://doi.org/10.1109/WACV45572.2020.9093288</a>'
  chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for
    Efficient Detection in Low-Resource Scenarios.” In <i>IEEE Winter Conference on
    Applications of Computer Vision</i>. IEEE, 2020. <a href="https://doi.org/10.1109/WACV45572.2020.9093288">https://doi.org/10.1109/WACV45572.2020.9093288</a>.
  ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection
    in low-resource scenarios,” in <i>IEEE Winter Conference on Applications of Computer
    Vision</i>,  Snowmass Village, CO, United States, 2020.
  ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection
    in low-resource scenarios. IEEE Winter Conference on Applications of Computer
    Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.'
  mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient
    Detection in Low-Resource Scenarios.” <i>IEEE Winter Conference on Applications
    of Computer Vision</i>, 1716–1725, IEEE, 2020, doi:<a href="https://doi.org/10.1109/WACV45572.2020.9093288">10.1109/WACV45572.2020.9093288</a>.
  short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer
    Vision, IEEE, 2020.
conference:
  end_date: 2020-03-05
  location: ' Snowmass Village, CO, United States'
  name: 'WACV: Winter Conference on Applications of Computer Vision'
  start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093288
external_id:
  arxiv:
  - '2004.12623'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2004.12623
month: '03'
oa: 1
oa_version: Preprint
publication: IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
  isbn:
  - '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
  record:
  - id: '8331'
    relation: dissertation_contains
    status: deleted
  - id: '8390'
    relation: dissertation_contains
    status: public
scopus_import: 1
status: public
title: Localizing grouped instances for efficient detection in low-resource scenarios
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '7937'
abstract:
- lang: eng
  text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained
    convolutional network for a new visual recognition task. However, the orthogonal
    setting of transferring knowledge from a pretrained network to a visually different
    yet semantically close source is rarely considered: This commonly happens with
    real-life data, which is not necessarily as clean as the training source (noise,
    geometric transformations, different modalities, etc.).To tackle such scenarios,
    we introduce a new, generalized form of fine-tuning, called flex-tuning, in which
    any individual unit (e.g. layer) of a network can be tuned, and the most promising
    one is chosen automatically. In order to make the method appealing for practical
    use, we propose two lightweight and faster selection procedures that prove to
    be good approximations in practice. We study these selection criteria empirically
    across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning
    individual units, despite its simplicity, yields very good results as an adaptation
    technique. As it turns out, in contrast to common practice, rather than the last
    fully-connected unit it is best to tune an intermediate or early one in many domain-
    shift scenarios, which is accurately detected by flex-tuning.'
article_number: 2180-2189
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
  full_name: Royer, Amélie
  id: 3811D890-F248-11E8-B48F-1D18A9856A87
  last_name: Royer
  orcid: 0000-0002-8407-0705
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer
    learning. In: <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>.
    IEEE; 2020. doi:<a href="https://doi.org/10.1109/WACV45572.2020.9093635">10.1109/WACV45572.2020.9093635</a>'
  apa: 'Royer, A., &#38; Lampert, C. (2020). A flexible selection scheme for minimum-effort
    transfer learning. In <i>2020 IEEE Winter Conference on Applications of Computer
    Vision</i>. Snowmass Village, CO, United States: IEEE. <a href="https://doi.org/10.1109/WACV45572.2020.9093635">https://doi.org/10.1109/WACV45572.2020.9093635</a>'
  chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for
    Minimum-Effort Transfer Learning.” In <i>2020 IEEE Winter Conference on Applications
    of Computer Vision</i>. IEEE, 2020. <a href="https://doi.org/10.1109/WACV45572.2020.9093635">https://doi.org/10.1109/WACV45572.2020.9093635</a>.
  ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer
    learning,” in <i>2020 IEEE Winter Conference on Applications of Computer Vision</i>,
    Snowmass Village, CO, United States, 2020.
  ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort
    transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision.
    WACV: Winter Conference on Applications of Computer Vision, 2180–2189.'
  mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort
    Transfer Learning.” <i>2020 IEEE Winter Conference on Applications of Computer
    Vision</i>, 2180–2189, IEEE, 2020, doi:<a href="https://doi.org/10.1109/WACV45572.2020.9093635">10.1109/WACV45572.2020.9093635</a>.
  short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of
    Computer Vision, IEEE, 2020.
conference:
  end_date: 2020-03-05
  location: Snowmass Village, CO, United States
  name: 'WACV: Winter Conference on Applications of Computer Vision'
  start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2023-09-07T13:16:17Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093635
external_id:
  arxiv:
  - '2008.11995'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://arxiv.org/abs/2008.11995
month: '03'
oa: 1
oa_version: Preprint
publication: 2020 IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
  isbn:
  - '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
  record:
  - id: '8331'
    relation: dissertation_contains
    status: deleted
  - id: '8390'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: A flexible selection scheme for minimum-effort transfer learning
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...