| 1. |
- Edstedt, Johan, et al.
(författare)
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VidHarm: A Clip Based Dataset for Harmful Content Detection
- 2022
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Ingår i: 2022 26th International Conference on Pattern Recognition (ICPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665490627 - 9781665490634 ; , s. 1543-1549
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Konferensbidrag (refereegranskat)abstract
- Automatically identifying harmful content in video is an important task with a wide range of applications. However, there is a lack of professionally labeled open datasets available. In this work VidHarm, an open dataset of 3589 video clips from film trailers annotated by professionals, is presented. An analysis of the dataset is performed, revealing among other things the relation between clip and trailer level annotations. Audiovisual models are trained on the dataset and an in-depth study of modeling choices conducted. The results show that performance is greatly improved by combining the visual and audio modality, pre-training on large-scale video recognition datasets, and class balanced sampling. Lastly, biases of the trained models are investigated using discrimination probing.VidHarm is openly available, and further details are available at the webpage https://vidharm.github.io/
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| 2. |
- Ihre, Johan, 1707-1780
(författare)
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Dissertatio philosophica, de infelicitate hominis æterna ex ratione demonstrabili, quam ... præside ... Johanne Ihre, ... pro gradu, publicæ disquisitioni modeste submittit stipendiarius regius, Petrus Edstedt, Vermelandus. In Audit. Carol. Majori ad diem [24] Maji. Anni MDCCXLVI. Horis, ante meridiem, consuetis.
- 1746
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
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| 3. |
- Ihre, Johan, 1707-1780
(författare)
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Exercitium academicum, de jure puniendi in statu naturali, quod consentient. ampliss. Senatu Philosoph. in illustri Reg. Academia Upsaliensi, præside ... Johanne Ihre, ... bonorum examini modeste submittit Stipendiarius Regius, Petrus Edstedt, Wermelandus. In Audit. Carol. Majori ad diem [27] Octob. anni MDCCXLIV. Horis, ante meridiem, consvetis.
- 1744
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)
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| 4. |
- Brissman, Emil, 1987-, et al.
(författare)
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Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods
- 2023
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Konferensbidrag (refereegranskat)abstract
- A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models’ residual variances—however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.
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| 5. |
- Edstedt, Johan, Doktorand, et al.
(författare)
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DeDoDe: Detect, Don’t Describe — Describe, Don’t Detect for Local Feature Matching
- 2024
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Ingår i: 2024 International Conference on 3D Vision (3DV). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350362459 - 9798350362466
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Konferensbidrag (refereegranskat)abstract
- Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene. One of the main challenges with keypoint detection is the formulation of the learning objective. Previous learning-based methods typically jointly learn descriptors with keypoints, and treat the keypoint detection as a binary classification task on mutual nearest neighbours. However, basing keypoint detection on descriptor nearest neighbours is a proxy task, which is not guaranteed to produce 3D-consistent keypoints. Furthermore, this ties the keypoints to a specific descriptor, complicating downstream usage. In this work, we instead learn keypoints directly from 3D consistency. To this end, we train the detector to detect tracks from large-scale SfM. As these points are often overly sparse, we derive a semi-supervised two-view detection objective to expand this set to a desired number of detections. To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network. Results show that our approach, DeDoDe, achieves significant gains on multiple geometry benchmarks. Code is provided at http://github.com/Parskatt/DeDoDegithub.com/Parskatt/DeDoDe.
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| 6. |
- Edstedt, Johan, et al.
(författare)
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DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching
- 2024
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Ingår i: Proceedings - 2024 International Conference on 3D Vision, 3DV 2024. ; , s. 148-157
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Konferensbidrag (refereegranskat)abstract
- Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene. One of the main challenges with keypoint detection is the formulation of the learning objective. Previous learning-based methods typically jointly learn descriptors with keypoints, and treat the keypoint detection as a binary classification task on mutual nearest neighbours. However, basing keypoint detection on descriptor nearest neighbours is a proxy task, which is not guaranteed to produce 3D-consistent keypoints. Furthermore, this ties the keypoints to a specific descriptor, complicating downstream usage. In this work, we instead learn keypoints directly from 3D consistency. To this end, we train the detector to detect tracks from large-scale SfM. As these points are often overly sparse, we derive a semi-supervised two-view detection objective to expand this set to a desired number of detections. To train a descriptor, we maximize the mutual nearest neighbour objective over the keypoints with a separate network. Results show that our approach, DeDoDe, achieves significant gains on multiple geometry benchmarks. Code is provided at http://github.com/Parskatt/DeDoDegithub.com/Parskatt/DeDoDe.
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| 7. |
- Edstedt, Johan, Doktorand, et al.
(författare)
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DKM: Dense Kernelized Feature Matching for Geometry Estimation
- 2023
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Ingår i: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). - : IEEE Communications Society. - 9798350301298 - 9798350301304 ; , s. 17765-17775
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Konferensbidrag (refereegranskat)abstract
- Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, Dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on MegaDepth-1500 of +4.9 and +8.9 AUC@5° compared to the best previous sparse method and dense method respectively. Our code is provided at the following repository: https://github.com/Parskatt/DKM.
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| 8. |
- Johnander, Joakim, et al.
(författare)
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Dense Gaussian Processes for Few-Shot Segmentation
- 2022
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Ingår i: COMPUTER VISION, ECCV 2022, PT XXIX. - Cham : SPRINGER INTERNATIONAL PUBLISHING AG. - 9783031198175 - 9783031198182 ; , s. 217-234
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Konferensbidrag (refereegranskat)abstract
- Few-shot segmentation is a challenging dense prediction task, which entails segmenting a novel query image given only a small annotated support set. The key problem is thus to design a method that aggregates detailed information from the support set, while being robust to large variations in appearance and context. To this end, we propose a few-shot segmentation method based on dense Gaussian process (GP) regression. Given the support set, our dense GP learns the mapping from local deep image features to mask values, capable of capturing complex appearance distributions. Furthermore, it provides a principled means of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. Instead of a one-dimensional mask output, we further exploit the end-to-end learning capabilities of our approach to learn a high-dimensional output space for the GP. Our approach sets a new state-of-the-art on the PASCAL-5(i) and COCO-20(i) benchmarks, achieving an absolute gain of +8.4 mIoU in the COCO-20(i) 5-shot setting. Furthermore, the segmentation quality of our approach scales gracefully when increasing the support set size, while achieving robust cross-dataset transfer.
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