| 1. |
- Abdelnour, Jerome, et al.
(författare)
-
NAAQA: A Neural Architecture for Acoustic Question Answering
- 2022
-
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
-
Tidskriftsartikel (refereegranskat)
|
|
| 2. |
- Balgi, Sourabh, 1991-, et al.
(författare)
-
Contradistinguisher : A Vapnik’s Imperative to Unsupervised Domain Adaptation
- 2022
-
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
-
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.
|
|
| 3. |
- Cao, Jiale, et al.
(författare)
-
From Handcrafted to Deep Features for Pedestrian Detection : A Survey
- 2022
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - New York : IEEE. - 0162-8828 .- 1939-3539. ; 44:9, s. 4913-4934
-
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.
|
|
| 4. |
- Cao, Jiale, et al.
(författare)
-
SipMaskv2: Enhanced Fast Image and Video Instance Segmentation
- 2023
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE. - 0162-8828 .- 1939-3539 .- 2160-9292. ; 45:3, s. 3798-3812
-
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.
|
|
| 5. |
- Dombrowski, Ann Kathrin, et al.
(författare)
-
Diffeomorphic Counterfactuals with Generative Models
- 2024
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 46:5, s. 3257-3274
-
Tidskriftsartikel (refereegranskat)abstract
- Counterfactuals can explain classification decisions of neural networks in a human interpretable way. We propose a simple but effective method to generate such counterfactuals. More specifically, we perform a suitable diffeomorphic coordinate transformation and then perform gradient ascent in these coordinates to find counterfactuals which are classified with great confidence as a specified target class. We propose two methods to leverage generative models to construct such suitable coordinate systems that are either exactly or approximately diffeomorphic. We analyze the generation process theoretically using Riemannian differential geometry and validate the quality of the generated counterfactuals using various qualitative and quantitative measures.
|
|
| 6. |
- Duff, Timothy, et al.
(författare)
-
PLMP : Point-Line Minimal Problems in Complete Multi-View Visibility
- 2024
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 46:1, s. 421-435
-
Tidskriftsartikel (refereegranskat)abstract
- We present a complete classification of all minimal problems for generic arrangements of points and lines completely observed by calibrated perspective cameras. We show that there are only 30 minimal problems in total, no problems exist for more than 6 cameras, for more than 5 points, and for more than 6 lines. We present a sequence of tests for detecting minimality starting with counting degrees of freedom and ending with full symbolic and numeric verification of representative examples. For all minimal problems discovered, we present their algebraic degrees, i.e.the number of solutions, which measure their intrinsic difficulty. It shows how exactly the difficulty of problems grows with the number of views. Importantly, several new minimal problems have small degrees that might be practical in image matching and 3D reconstruction.
|
|
| 7. |
- Eldesokey, Abdelrahman, et al.
(författare)
-
Confidence Propagation through CNNs for Guided Sparse Depth Regression
- 2020
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE. - 0162-8828 .- 1939-3539. ; 42:10
-
Tidskriftsartikel (refereegranskat)abstract
- Generally, convolutional neural networks (CNNs) process data on a regular grid, e.g. data generated by ordinary cameras. Designing CNNs for sparse and irregularly spaced input data is still an open research problem with numerous applications in autonomous driving, robotics, and surveillance. In this paper, we propose an algebraically-constrained normalized convolution layer for CNNs with highly sparse input that has a smaller number of network parameters compared to related work. We propose novel strategies for determining the confidence from the convolution operation and propagating it to consecutive layers. We also propose an objective function that simultaneously minimizes the data error while maximizing the output confidence. To integrate structural information, we also investigate fusion strategies to combine depth and RGB information in our normalized convolution network framework. In addition, we introduce the use of output confidence as an auxiliary information to improve the results. The capabilities of our normalized convolution network framework are demonstrated for the problem of scene depth completion. Comprehensive experiments are performed on the KITTI-Depth and the NYU-Depth-v2 datasets. The results clearly demonstrate that the proposed approach achieves superior performance while requiring only about 1-5% of the number of parameters compared to the state-of-the-art methods.
|
|
| 8. |
- Eriksson, Anders, et al.
(författare)
-
Rotation Averaging with the Chordal Distance: Global Minimizers and Strong Duality
- 2021
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - 1939-3539 .- 0162-8828. ; 43:1, s. 256-268
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper we explore the role of duality principles within the problem of rotation averaging, a fundamental task in a wide range of applications. In its conventional form, rotation averaging is stated as a minimization over multiple rotation constraints. As these constraints are non-convex, this problem is generally considered challenging to solve globally. We show how to circumvent this difficulty through the use of Lagrangian duality. While such an approach is well-known it is normally not guaranteed to provide a tight relaxation. Based on spectral graph theory, we analytically prove that in many cases there is no duality gap unless the noise levels are severe. This allows us to obtain certifiably global solutions to a class of important non-convex problems in polynomial time. We also propose an efficient, scalable algorithm that outperforms general purpose numerical solvers by a large margin and compares favourably to current state-of-the-art. Further, our approach is able to handle the large problem instances commonly occurring in structure from motion settings and it is trivially parallelizable. Experiments are presented for a number of different instances of both synthetic and real-world data.
|
|
| 9. |
- Evain, S., et al.
(författare)
-
A Lightweight Neural Network for Monocular View Generation with Occlusion Handling
- 2021
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE Computer Society. - 0162-8828 .- 1939-3539. ; 43:6, s. 1832-1844
-
Tidskriftsartikel (refereegranskat)abstract
- In this article, we present a very lightweight neural network architecture, trained on stereo data pairs, which performs view synthesis from one single image. With the growing success of multi-view formats, this problem is indeed increasingly relevant. The network returns a prediction built from disparity estimation, which fills in wrongly predicted regions using a occlusion handling technique. To do so, during training, the network learns to estimate the left-right consistency structural constraint on the pair of stereo input images, to be able to replicate it at test time from one single image. The method is built upon the idea of blending two predictions: a prediction based on disparity estimation and a prediction based on direct minimization in occluded regions. The network is also able to identify these occluded areas at training and at test time by checking the pixelwise left-right consistency of the produced disparity maps. At test time, the approach can thus generate a left-side and a right-side view from one input image, as well as a depth map and a pixelwise confidence measure in the prediction. The work outperforms visually and metric-wise state-of-the-art approaches on the challenging KITTI dataset, all while reducing by a very significant order of magnitude (5 or 10 times) the required number of parameters (6.5 M). © 1979-2012 IEEE.
|
|
| 10. |
- Fukui, Kazuhiro, et al.
(författare)
-
Discriminant feature extraction by generalized difference subspace
- 2023
-
Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : Institute of Electrical and Electronics Engineers (IEEE). - 0162-8828 .- 1939-3539. ; 45:2, s. 1618-1635
-
Tidskriftsartikel (refereegranskat)abstract
- This paper reveals the discriminant ability of the orthogonal projection of data onto a generalized difference subspace (GDS) both theoretically and experimentally. In our previous work, we have demonstrated that GDS projection works as the quasi-orthogonalization of class subspaces. Interestingly, GDS projection also works as a discriminant feature extraction through a similar mechanism to the Fisher discriminant analysis (FDA). A direct proof of the connection between GDS projection and FDA is difficult due to the significant difference in their formulations. To avoid the difficulty, we first introduce geometrical Fisher discriminant analysis (gFDA) based on a simplified Fisher criterion. gFDA can work stably even under few samples, bypassing the small sample size (SSS) problem of FDA. Next, we prove that gFDA is equivalent to GDS projection with a small correction term. This equivalence ensures GDS projection to inherit the discriminant ability from FDA via gFDA. Furthermore, we discuss two useful extensions of these methods, 1) nonlinear extension by kernel trick, 2) the combination of convolutional neural network (CNN) features. The equivalence and the effectiveness of the extensions have been verified through extensive experiments on the extended Yale B+, CMU face database, ALOI, ETH80, MNIST and CIFAR10, focusing on the SSS problem. IEEE
|
|
