| 1. |
- Abdelnour, Jerome, et al.
(författare)
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NAAQA: A Neural Architecture for Acoustic Question Answering
- 2022
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539 .- 2160-9292. ; , s. 1-12
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Azizpour, Hossein, 1985-, et al.
(författare)
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Factors of Transferability for a Generic ConvNet Representation
- 2016
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society Digital Library. - 0162-8828 .- 1939-3539. ; 38:9, s. 1790-1802
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Tidskriftsartikel (refereegranskat)abstract
- Evidence is mounting that Convolutional Networks (ConvNets) are the most effective representation learning method for visual recognition tasks. In the common scenario, a ConvNet is trained on a large labeled dataset (source) and the feed-forward units activation of the trained network, at a certain layer of the network, is used as a generic representation of an input image for a task with relatively smaller training set (target). Recent studies have shown this form of representation transfer to be suitable for a wide range of target visual recognition tasks. This paper introduces and investigates several factors affecting the transferability of such representations. It includes parameters for training of the source ConvNet such as its architecture, distribution of the training data, etc. and also the parameters of feature extraction such as layer of the trained ConvNet, dimensionality reduction, etc. Then, by optimizing these factors, we show that significant improvements can be achieved on various (17) visual recognition tasks. We further show that these visual recognition tasks can be categorically ordered based on their similarity to the source task such that a correlation between the performance of tasks and their similarity to the source task w.r.t. the proposed factors is observed.
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| 3. |
- Balgi, Sourabh, 1991-, et al.
(författare)
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Contradistinguisher : A Vapnik’s Imperative to Unsupervised Domain Adaptation
- 2022
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - Piscataway, NJ, United States : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 44:9, s. 4730-4747
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Tidskriftsartikel (refereegranskat)abstract
- Recent domain adaptation works rely on an indirect way of first aligning the source and target domain distributions and then train a classifier on the labeled source domain to classify the target domain. However, the main drawback of this approach is that obtaining a near-perfect domain alignment in itself might be difficult/impossible (e.g., language domains). To address this, inspired by how humans use supervised-unsupervised learning to perform tasks seamlessly across multiple domains or tasks, we follow Vapnik’s imperative of statistical learning that states any desired problem should be solved in the most direct way rather than solving a more general intermediate task and propose a direct approach to domain adaptation that does not require domain alignment. We propose a model referred to as Contradistinguisher that learns contrastive features and whose objective is to jointly learn to contradistinguish the unlabeled target domain in an unsupervised way and classify in a supervised way on the source domain. We achieve the state-of-the-art on Office-31, Digits and VisDA-2017 datasets in both single-source and multi-source settings. We demonstrate that performing data augmentation results in an improvement in the performance over vanilla approach. We also notice that the contradistinguish-loss enhances performance by increasing the shape bias.
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| 4. |
- Bigun, Josef, et al.
(författare)
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Multidimensional orientation estimation with applications to texture analysis and optical flow
- 1991
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 13:8, s. 775-790
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Tidskriftsartikel (refereegranskat)abstract
- The problem of detection of orientation in finite dimensional Euclidean spaces is solved in the least squares sense. In particular, the theory is developed for the case when such orientation computations are necessary at all local neighborhoods of the n-dimensional Euclidean space. Detection of orientation is shown to correspond to fitting an axis or a plane to the Fourier transform of an n-dimensional structure. The solution of this problem is related to the solution of a well-known matrix eigenvalue problem. Moreover, it is shown that the necessary computations can be performed in the spatial domain without actually doing a Fourier transformation. Along with the orientation estimate, a certainty measure, based on the error of the fit, is proposed. Two applications in image analysis are considered: texture segmentation and optical flow. An implementation for 2-D (texture features) as well as 3-D (optical flow) is presented. In the case of 2-D, the method exploits the properties of the complex number field to by-pass the eigenvalue analysis, improving the speed and the numerical stability of the method. The theory is verified by experiments which confirm accurate orientation estimates and reliable certainty measures in the presence of noise. The comparative results indicate that the proposed theory produces algorithms computing robust texture features as well as optical flow. The computations are highly parallelizable and can be used in realtime image analysis since they utilize only elementary functions in a closed form (up to dimension 4) and Cartesian separable convolutions.
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| 5. |
- Bigun, Josef, 1961-, et al.
(författare)
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Recognition by symmetry derivatives and the generalized structure tensor
- 2004
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - Los Alamitos, USA : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 26:12, s. 1590-1605
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Tidskriftsartikel (refereegranskat)abstract
- We suggest a set of complex differential operators that can be used to produce and filter dense orientation (tensor) fields for feature extraction, matching, and pattern recognition. We present results on the invariance properties of these operators, that we call symmetry derivatives. These show that, in contrast to ordinary derivatives, all orders of symmetry derivatives of Gaussians yield a remarkable invariance: they are obtained by replacing the original differential polynomial with the same polynomial, but using ordinary coordinates x and y corresponding to partial derivatives. Moreover, the symmetry derivatives of Gaussians are closed under the convolution operator and they are invariant to the Fourier transform. The equivalent of the structure tensor, representing and extracting orientations of curve patterns, had previously been shown to hold in harmonic coordinates in a nearly identical manner. As a result, positions, orientations, and certainties of intricate patterns, e.g., spirals, crosses, parabolic shapes, can be modeled by use of symmetry derivatives of Gaussians with greater analytical precision as well as computational efficiency. Since Gaussians and their derivatives are utilized extensively in image processing, the revealed properties have practical consequences for local orientation based feature extraction. The usefulness of these results is demonstrated by two applications:tracking cross markers in long image sequences from vehicle crash tests andalignment of noisy fingerprints.
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| 6. |
- Björkman, Mårten, et al.
(författare)
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Real-time epipolar geometry estimation of binocular stereo heads
- 2002
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 24:3, s. 425-432
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Tidskriftsartikel (refereegranskat)abstract
- Stereo is an important cue for visually guided robots. While moving around in the world, such a robot can use dynamic fixation to overcome limitations in image resolution and field of view. In this paper, a binocular stereo system capable of dynamic fixation is presented. The external calibration is performed continuously taking temporal consistency into consideration, greatly simplifying the process. The essential matrix, which is estimated in real-time, is used to describe the epipolar geometry. It will be shown, how outliers can be identified and excluded from the calculations. An iterative approach based on a differential model of the optical flow, commonly used in structure from motion, is also presented and tested towards the essential matrix. The iterative method will be shown to be superior in terms of both computational speed and robustness, when the vergence angles are less than about 15degrees. For larger angles, the differential model is insufficient and the essential matrix is preferably used instead.
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| 7. |
- Cao, Jiale, et al.
(författare)
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From Handcrafted to Deep Features for Pedestrian Detection : A Survey
- 2022
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - New York : IEEE. - 0162-8828 .- 1939-3539. ; 44:9, s. 4913-4934
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Tidskriftsartikel (refereegranskat)abstract
- Pedestrian detection is an important but challenging problem in computer vision, especially in human-centric tasks. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. Here we present a comprehensive survey on recent advances in pedestrian detection. First, we provide a detailed review of single-spectral pedestrian detection that includes handcrafted features based methods and deep features based approaches. For handcrafted features based methods, we present an extensive review of approaches and find that handcrafted features with large freedom degrees in shape and space have better performance. In the case of deep features based approaches, we split them into pure CNN based methods and those employing both handcrafted and CNN based features. We give the statistical analysis and tendency of these methods, where feature enhanced, part-aware, and post-processing methods have attracted main attention. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection, which provides more robust features for illumination variance. Furthermore, we introduce some related datasets and evaluation metrics, and a deep experimental analysis. We conclude this survey by emphasizing open problems that need to be addressed and highlighting various future directions. Researchers can track an up-to-date list at https://github.com/JialeCao001/PedSurvey.
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| 8. |
- Cao, Jiale, et al.
(författare)
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SipMaskv2: Enhanced Fast Image and Video Instance Segmentation
- 2023
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE. - 0162-8828 .- 1939-3539 .- 2160-9292. ; 45:3, s. 3798-3812
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Tidskriftsartikel (refereegranskat)abstract
- We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for the sub-regions within a bounding-box, enabling a better delineation of spatially adjacent instances. To better correlate mask prediction with object detection, we further propose a mask alignment weighting loss and a feature alignment scheme. In addition, we identify two issues that impede the performance of single-stage instance segmentation and introduce two modules, including a sample selection scheme and an instance refinement module, to address these two issues. Experiments are performed on both image instance segmentation dataset MS COCO and video instance segmentation dataset YouTube-VIS. On MS COCO test-dev set, our method achieves a state-of-the-art performance. In terms of real-time capabilities, it outperforms YOLACT by a gain of 3.0% (mask AP) under the similar settings, while operating at a comparable speed. On YouTube-VIS validation set, our method also achieves promising results. The source code is available at https://github.com/JialeCao001/SipMask.
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| 9. |
- Danelljan, Martin, 1989-, et al.
(författare)
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Discriminative Scale Space Tracking
- 2017
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 39:8, s. 1561-1575
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Tidskriftsartikel (refereegranskat)abstract
- Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014.
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| 10. |
- Demisse, G. G., et al.
(författare)
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Deformation Based Curved Shape Representation
- 2018
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 40:6, s. 1338-1351
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Tidskriftsartikel (refereegranskat)abstract
- n this paper, we introduce a deformation based representation space for curved shapes in R-n. Given an ordered set of points sampled from a curved shape, the proposed method represents the set as an element of a finite dimensional matrix Lie group. Variation due to scale and location are filtered in a preprocessing stage, while shapes that vary only in rotation are identified by an equivalence relationship. The use of a finite dimensional matrix Lie group leads to a similarity metric with an explicit geodesic solution. Subsequently, we discuss some of the properties of the metric and its relationship with a deformation by least action. Furthermore, invariance to reparametrization or estimation of point correspondence between shapes is formulated as an estimation of sampling function. Thereafter, two possible approaches are presented to solve the point correspondence estimation problem. Finally, we propose an adaptation of k-means clustering for shape analysis in the proposed representation space. Experimental results show that the proposed representation is robust to uninformative cues, e.g., local shape perturbation and displacement. In comparison to state of the art methods, it achieves a high precision on the Swedish and the Flavia leaf datasets and a comparable result on MPEG-7, Kimia99 and Kimia216 datasets.
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