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| 2. |
- Broomé, Per, et al.
(författare)
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Vita fläckar : Om integrationspolitik, ledning, och mångfald i Malmö stad
- 2007
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Ingår i: Vita fläckar. - : School of International Migration and Ethnic Relations (IMER). - 9789171040749 ; , s. 7-16
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Malmö has a long history of immigration and integration work. Ambitions have kept pace with immigration and are high among the political leadership and many managers of municipal organisations. Experience relating to different integration policy ideas and practical integration work is considerable. It is important to stress that our presentation of critical analyses of integration policy and diversity management is by no means a denigration of these ambitions or insights, but rather indicates the difficulties of implementing integration policy and diversity management measures in a large municipal organisation. The municipal authority's integration experiences lead to the continual revision of its own internal integration policies, of which the diversity idea and diversity management are included. Revisions, new ideas and measures – ad infinitum – seem to be natural features of integration policy. This is particularly evident in the political rhetoric, as well as in the different integration plans and measures. One reason for this is that integration and diversity issues are dealt with reactively as problems arise, i.e. the discovery of a problem triggers some kind of action. In contrast, the opportunities and possibilities that human diversity offers are given very little attention. The three articles appearing in this issue describe and analyse how diversity issues have difficulty in being included in management practices and how the discoveries of diversity and its content are either curtailed or fail to materialise in the organisation of the City of Malmö. The first article describes how integration policy alternatives arising from the diversity idea are difficult to establish in relation to, for example, alternatives relating to the introduction programme and employment opportunities for immigrants. The issue of diversity is a white spot in the municipality's integration policy. One myth about the white spot's content, and which affects integration policy, is that the potential of diversity is known, culturally ordered and can be quantitatively represented. Another myth is that so called social engineering can deal with the content of the white spot. A similar problem arises in the second article, when the diversity issue is identified as something primarily concerned with "arranging diversity in society", while diversity issues are dismissed in the internal organisation. Internal diversity issues thus become a white spot for managers. One myth, which affects the organisation, is that the content of the internal white spot is charted and included in the organisation of its own accord. The third article deals with the struggle between whether it is core work or the diversity perspective that is in focus. Core work is experienced by managers as the most central. The diversity issue thus becomes a white spot in that it is placed outside the core work frame. One myth is that the unfamiliar content of the white spot can be prevented from impacting the existing organisation. All three articles point to the fact that managers and political leaders have difficulty in identifying the benefits of diversity for the internal and external organisation in anything other than general terms. This observation probably reveals an important reason as to why the diversity issue has not been picked up by management and put into practice.
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| 3. |
- Broomé, Sofia, et al.
(författare)
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Dynamics are important for the recognition of equine pain in video
- 2019
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Ingår i: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6919.
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Konferensbidrag (refereegranskat)abstract
- A prerequisite to successfully alleviate pain in animals is to recognize it, which is a great challenge in non-verbal species. Furthermore, prey animals such as horses tend to hide their pain. In this study, we propose a deep recurrent two-stream architecture for the task of distinguishing pain from non-pain in videos of horses. Different models are evaluated on a unique dataset showing horses under controlled trials with moderate pain induction, which has been presented in earlier work. Sequential models are experimentally compared to single-frame models, showing the importance of the temporal dimension of the data, and are benchmarked against a veterinary expert classification of the data. We additionally perform baseline comparisons with generalized versions of state-of-the-art human pain recognition methods. While equine pain detection in machine learning is a novel field, our results surpass veterinary expert performance and outperform pain detection results reported for other larger non-human species.
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| 4. |
- Broomé, Sofia, 1990-
(författare)
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Learning Spatiotemporal Features in Low-Data and Fine-Grained Action Recognition with an Application to Equine Pain Behavior
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recognition of pain in animals is important because pain compromises animal welfare and can be a manifestation of disease. This is a difficult task for veterinarians and caretakers, partly because horses, being prey animals, display subtle pain behavior, and because they cannot verbalize their pain. An automated video-based system has a large potential to improve the consistency and efficiency of pain predictions.Video recording is desirable for ethological studies because it interferes minimally with the animal, in contrast to more invasive measurement techniques, such as accelerometers. Moreover, to be able to say something meaningful about animal behavior, the subject needs to be studied for longer than the exposure of single images. In deep learning, we have not come as far for video as we have for single images, and even more questions remain regarding what types of architectures should be used and what these models are actually learning. Collecting video data with controlled moderate pain labels is both laborious and involves real animals, and the amount of such data should therefore be limited. The low-data scenario, in particular, is under-explored in action recognition, in favor of the ongoing exploration of how well large models can learn large datasets.The first theme of the thesis is automated recognition of equine pain. Here, we propose a method for end-to-end equine pain recognition from video, finding, in particular, that the temporal modeling ability of the artificial neural network is important to improve the classification. We surpass veterinarian experts on a dataset with horses undergoing well-defined moderate experimental pain induction. Next, we investigate domain transfer to another type of pain in horses: less defined, longer-acting and lower-grade orthopedic pain. We find that a smaller, recurrent video model is more robust to domain shift on a target dataset than a large, pre-trained, 3D CNN, having equal performance on a source dataset. We also discuss challenges with learning video features on real-world datasets.Motivated by questions arisen within the application area, the second theme of the thesis is empirical properties of deep video models. Here, we study the spatiotemporal features that are learned by deep video models in end-to-end video classification and propose an explainability method as a tool for such investigations. Further, the question of whether different approaches to frame dependency treatment in video models affect their cross-domain generalization ability is explored through empirical study. We also propose new datasets for light-weight temporal modeling and to investigate texture bias within action recognition.
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| 5. |
- Broomé, Sofia, et al.
(författare)
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Recur, Attend or Convolve? : On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition
- 2023
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Ingår i: 2023 IEEE/CVF WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 4188-4198
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Konferensbidrag (refereegranskat)abstract
- Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes. It has also been shown that 2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather than shape in still image recognition tasks [19], in contrast to humans. Taken together, this raises suspicion that large video models partly learn spurious spatial texture correlations rather than to track relevant shapes over time to infer generalizable semantics from their movement. A natural way to avoid parameter explosion when learning visual patterns over time is to make use of recurrence. Biological vision consists of abundant recurrent circuitry, and is superior to computer vision in terms of domain shift generalization. In this article, we empirically study whether the choice of low-level temporal modeling has consequences for texture bias and cross-domain robustness. In order to enable a light-weight and systematic assessment of the ability to capture temporal structure, not revealed from single frames, we provide the Temporal Shape (TS) dataset, as well as modified domains of Diving48 allowing for the investigation of spatial texture bias in video models. The combined results of our experiments indicate that sound physical inductive bias such as recurrence in temporal modeling may be advantageous when robustness to domain shift is important for the task.
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| 6. |
- Broomé, Sofia, et al.
(författare)
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Sharing pain : Using pain domain transfer for video recognition of low grade orthopedic pain in horses
- 2022
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 17:3, s. e0263854-
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Tidskriftsartikel (refereegranskat)abstract
- Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
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| 7. |
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| 8. |
- Haubro Andersen, Pia, et al.
(författare)
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Towards Machine Recognition of Facial Expressions of Pain in Horses
- 2021
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Ingår i: Animals. - : MDPI. - 2076-2615. ; 11:6
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Forskningsöversikt (refereegranskat)abstract
- Simple Summary Facial activity can convey valid information about the experience of pain in a horse. However, scoring of pain in horses based on facial activity is still in its infancy and accurate scoring can only be performed by trained assessors. Pain in humans can now be recognized reliably from video footage of faces, using computer vision and machine learning. We examine the hurdles in applying these technologies to horses and suggest two general approaches to automatic horse pain recognition. The first approach involves automatically detecting objectively defined facial expression aspects that do not involve any human judgment of what the expression "means". Automated classification of pain expressions can then be done according to a rule-based system since the facial expression aspects are defined with this information in mind. The other involves training very flexible machine learning methods with raw videos of horses with known true pain status. The upside of this approach is that the system has access to all the information in the video without engineered intermediate methods that have filtered out most of the variation. However, a large challenge is that large datasets with reliable pain annotation are required. We have obtained promising results from both approaches. Automated recognition of human facial expressions of pain and emotions is to a certain degree a solved problem, using approaches based on computer vision and machine learning. However, the application of such methods to horses has proven difficult. Major barriers are the lack of sufficiently large, annotated databases for horses and difficulties in obtaining correct classifications of pain because horses are non-verbal. This review describes our work to overcome these barriers, using two different approaches. One involves the use of a manual, but relatively objective, classification system for facial activity (Facial Action Coding System), where data are analyzed for pain expressions after coding using machine learning principles. We have devised tools that can aid manual labeling by identifying the faces and facial keypoints of horses. This approach provides promising results in the automated recognition of facial action units from images. The second approach, recurrent neural network end-to-end learning, requires less extraction of features and representations from the video but instead depends on large volumes of video data with ground truth. Our preliminary results suggest clearly that dynamics are important for pain recognition and show that combinations of recurrent neural networks can classify experimental pain in a small number of horses better than human raters.
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| 9. |
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| 10. |
- Mänttäri, Joonatan, et al.
(författare)
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Interpreting Video Features : A Comparison of 3D Convolutional Networks and Convolutional LSTM Networks
- 2020
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Konferensbidrag (refereegranskat)abstract
- A number of techniques for interpretability have been presented for deep learning in computer vision, typically with the goal of understanding what the networks have based their classification on. However, interpretability for deep video architectures is still in its infancy and we do not yet have a clear concept of how to decode spatiotemporal features. In this paper, we present a study comparing how 3D convolutional networks and convolutional LSTM networks learn features across temporally dependent frames. This is the first comparison of two video models that both convolve to learn spatial features but have principally different methods of modeling time. Additionally, we extend the concept of meaningful perturbation introduced by Vedaldi et al. to the temporal dimension, to identify the temporal part of a sequence most meaningful to the network for a classification decision. Our findings indicate that the 3D convolutional model concentrates on shorter events in the input sequence, and places its spatial focus on fewer, contiguous areas.
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