| 1. |
- Felsberg, Michael, et al.
(författare)
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The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results
- 2015
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Ingår i: Proceedings of the IEEE International Conference on Computer Vision. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467383905 ; , s. 639-651
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Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.
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| 2. |
- Felsberg, Michael, 1974-, et al.
(författare)
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The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results
- 2016
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Ingår i: Computer Vision – ECCV 2016 Workshops. ECCV 2016.. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 824-849
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Konferensbidrag (refereegranskat)abstract
- The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
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| 3. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2013 challenge results
- 2013
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Ingår i: 2013 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE. - 9781479930227 ; , s. 98-111
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Konferensbidrag (refereegranskat)abstract
- Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website(1).
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| 4. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2015 challenge results
- 2015
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Ingår i: Proceedings 2015 IEEE International Conference on Computer Vision Workshops ICCVW 2015. - : IEEE. - 9780769557205 ; , s. 564-586
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1).
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| 5. |
- Kristan, Matej, et al.
(författare)
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The Visual Object Tracking VOT2016 Challenge Results
- 2016
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Ingår i: COMPUTER VISION - ECCV 2016 WORKSHOPS, PT II. - Cham : SPRINGER INT PUBLISHING AG. - 9783319488813 - 9783319488806 ; , s. 777-823
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Konferensbidrag (refereegranskat)abstract
- The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment.
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| 6. |
- Naseer, Muzammal, et al.
(författare)
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Cross-Domain Transferability of Adversarial Perturbations
- 2019
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Ingår i: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019). - : NEURAL INFORMATION PROCESSING SYSTEMS (NIPS).
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Konferensbidrag (refereegranskat)abstract
- Adversarial examples reveal the blind spots of deep neural networks (DNNs) and represent a major concern for security-critical applications. The transferability of adversarial examples makes real-world attacks possible in black-box settings, where the attacker is forbidden to access the internal parameters of the model. The underlying assumption in most adversary generation methods, whether learning an instance-specific or an instance-agnostic perturbation, is the direct or indirect reliance on the original domain-specific data distribution. In this work, for the first time, we demonstrate the existence of domain-invariant adversaries, thereby showing common adversarial space among different datasets and models. To this end, we propose a framework capable of launching highly transferable attacks that crafts adversarial patterns to mislead networks trained on entirely different domains. For instance, an adversarial function learned on Paintings, Cartoons or Medical images can successfully perturb ImageNet samples to fool the classifier, with success rates as high as similar to 99% (l(infinity) <= 10). The core of our proposed adversarial function is a generative network that is trained using a relativistic supervisory signal that enables domain-invariant perturbations. Our approach sets the new state-of-the-art for fooling rates, both under the white-box and black-box scenarios. Furthermore, despite being an instance-agnostic perturbation function, our attack outperforms the conventionally much stronger instance-specific attack methods.
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| 7. |
- Naseer, Muzammal, et al.
(författare)
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Guidance Through Surrogate: Toward a Generic Diagnostic Attack
- 2022
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Ingår i: IEEE Transactions on Neural Networks and Learning Systems. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2162-237X .- 2162-2388.
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Tidskriftsartikel (refereegranskat)abstract
- Adversarial training (AT) is an effective approach to making deep neural networks robust against adversarial attacks. Recently, different AT defenses are proposed that not only maintain a high clean accuracy but also show significant robustness against popular and well-studied adversarial attacks, such as projected gradient descent (PGD). High adversarial robustness can also arise if an attack fails to find adversarial gradient directions, a phenomenon known as "gradient masking." In this work, we analyze the effect of label smoothing on AT as one of the potential causes of gradient masking. We then develop a guided mechanism to avoid local minima during attack optimization, leading to a novel attack dubbed guided projected gradient attack (G-PGA). Our attack approach is based on a "match and deceive" loss that finds optimal adversarial directions through guidance from a surrogate model. Our modified attack does not require random restarts a large number of attack iterations or a search for optimal step size. Furthermore, our proposed G-PGA is generic, thus it can be combined with an ensemble attack strategy as we demonstrate in the case of auto-attack, leading to efficiency and convergence speed improvements. More than an effective attack, G-PGA can be used as a diagnostic tool to reveal elusive robustness due to gradient masking in adversarial defenses.
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| 8. |
- Naseer, Muzammal, et al.
(författare)
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On Generating Transferable Targeted Perturbations
- 2021
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Ingår i: 2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021). - : IEEE. - 9781665428125 - 9781665428132 ; , s. 7688-7697
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- While the untargeted black-box transferability of adversarial perturbations has been extensively studied before, changing an unseen model's decisions to a specific `targeted' class remains a challenging feat. In this paper, we propose a new generative approach for highly transferable targeted perturbations (\ours). We note that the existing methods are less suitable for this task due to their reliance on class-boundary information that changes from one model to another, thus reducing transferability. In contrast, our approach matches the perturbed image `distribution' with that of the target class, leading to high targeted transferability rates. To this end, we propose a new objective function that not only aligns the global distributions of source and target images, but also matches the local neighbourhood structure between the two domains. Based on the proposed objective, we train a generator function that can adaptively synthesize perturbations specific to a given input. Our generative approach is independent of the source or target domain labels, while consistently performs well against state-of-the-art methods on a wide range of attack settings. As an example, we achieve 32.63% target transferability from (an adversarially weak) VGG19BN to (a strong) WideResNet on ImageNet val. set, which is 4× higher than the previous best generative attack and 16× better than instance-specific iterative attack.
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| 9. |
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| 10. |
- Naseer, Muzammal, et al.
(författare)
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Stylized Adversarial Defense
- 2023
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 45:5, s. 6403-6414
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Tidskriftsartikel (refereegranskat)abstract
- Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the input images. To address this vulnerability, adversarial training creates perturbation patterns and includes them in the training set to robustify the model. In contrast to existing adversarial training methods that only use class-boundary information (e.g., using a cross-entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model. Specifically, we use the style and content information of the target sample from another class, alongside its class-boundary information to create adversarial perturbations. We apply our proposed multi-task objective in a deeply supervised manner, extracting multi-scale feature knowledge to create maximally separating adversaries. Subsequently, we propose a max-margin adversarial training approach that minimizes the distance between source image and its adversary and maximizes the distance between the adversary and the target image. Our adversarial training approach demonstrates strong robustness compared to state-of-the-art defenses, generalizes well to naturally occurring corruptions and data distributional shifts, and retains the models accuracy on clean examples.
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