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Author | David Masip; Agata Lapedriza; Jordi Vitria | ||||
Title | Boosted Online Learning for Face Recognition | Type | Journal Article | ||
Year | 2009 | Publication | IEEE Transactions on Systems, Man and Cybernetics part B | Abbreviated Journal | TSMCB |
Volume | 39 | Issue | 2 | Pages | 530–538 |
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Abstract | Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. | ||||
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ISSN | 1083–4419 | ISBN | Medium | ||
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Notes | OR;MV | Approved | no | ||
Call Number | BCNPCL @ bcnpcl @ MLV2009 | Serial | 1155 | ||
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Author | Akshita Gupta; Sanath Narayan; Salman Khan; Fahad Shahbaz Khan; Ling Shao; Joost Van de Weijer | ||||
Title | Generative Multi-Label Zero-Shot Learning | Type | Journal Article | ||
Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 45 | Issue | 12 | Pages | 14611-14624 |
Keywords | Generalized zero-shot learning; Multi-label classification; Zero-shot object detection; Feature synthesis | ||||
Abstract | Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods. | ||||
Address | December 2023 | ||||
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Notes | LAMP; PID2021-128178OB-I00 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 3853 | ||
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Author | Swathikiran Sudhakaran; Sergio Escalera; Oswald Lanz | ||||
Title | Gate-Shift-Fuse for Video Action Recognition | Type | Journal Article | ||
Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 45 | Issue | 9 | Pages | 10913-10928 |
Keywords | Action Recognition; Video Classification; Spatial Gating; Channel Fusion | ||||
Abstract | Convolutional Neural Networks are the de facto models for image recognition. However 3D CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved the same success on standard action recognition benchmarks. One of the main reasons for this reduced performance of 3D CNNs is the increased computational complexity requiring large scale annotated datasets to train them in scale. 3D kernel factorization approaches have been proposed to reduce the complexity of 3D CNNs. Existing kernel factorization approaches follow hand-designed and hard-wired techniques. In this paper we propose Gate-Shift-Fuse (GSF), a novel spatio-temporal feature extraction module which controls interactions in spatio-temporal decomposition and learns to adaptively route features through time and combine them in a data dependent manner. GSF leverages grouped spatial gating to decompose input tensor and channel weighting to fuse the decomposed tensors. GSF can be inserted into existing 2D CNNs to convert them into an efficient and high performing spatio-temporal feature extractor, with negligible parameter and compute overhead. We perform an extensive analysis of GSF using two popular 2D CNN families and achieve state-of-the-art or competitive performance on five standard action recognition benchmarks. | ||||
Address | 1 Sept. 2023 | ||||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ SEL2023 | Serial | 3814 | ||
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Author | Javier Selva; Anders S. Johansen; Sergio Escalera; Kamal Nasrollahi; Thomas B. Moeslund; Albert Clapes | ||||
Title | Video transformers: A survey | Type | Journal Article | ||
Year | 2023 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 45 | Issue | 11 | Pages | 12922-12943 |
Keywords | Artificial Intelligence; Computer Vision; Self-Attention; Transformers; Video Representations | ||||
Abstract | Transformer models have shown great success handling long-range interactions, making them a promising tool for modeling video. However, they lack inductive biases and scale quadratically with input length. These limitations are further exacerbated when dealing with the high dimensionality introduced by the temporal dimension. While there are surveys analyzing the advances of Transformers for vision, none focus on an in-depth analysis of video-specific designs. In this survey, we analyze the main contributions and trends of works leveraging Transformers to model video. Specifically, we delve into how videos are handled at the input level first. Then, we study the architectural changes made to deal with video more efficiently, reduce redundancy, re-introduce useful inductive biases, and capture long-term temporal dynamics. In addition, we provide an overview of different training regimes and explore effective self-supervised learning strategies for video. Finally, we conduct a performance comparison on the most common benchmark for Video Transformers (i.e., action classification), finding them to outperform 3D ConvNets even with less computational complexity. | ||||
Address | 1 Nov. 2023 | ||||
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Notes | HUPBA; no menciona | Approved | no | ||
Call Number | Admin @ si @ SJE2023 | Serial | 3823 | ||
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Author | Marc Masana; Xialei Liu; Bartlomiej Twardowski; Mikel Menta; Andrew Bagdanov; Joost Van de Weijer | ||||
Title | Class-incremental learning: survey and performance evaluation | Type | Journal Article | ||
Year | 2022 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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Abstract | For future learning systems incremental learning is desirable, because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data required to be stored -- also important when privacy limitations are imposed; and learning that more closely resembles human learning. The main challenge for incremental learning is catastrophic forgetting, which refers to the precipitous drop in performance on previously learned tasks after learning a new one. Incremental learning of deep neural networks has seen explosive growth in recent years. Initial work focused on task incremental learning, where a task-ID is provided at inference time. Recently we have seen a shift towards class-incremental learning where the learner must classify at inference time between all classes seen in previous tasks without recourse to a task-ID. In this paper, we provide a complete survey of existing methods for incremental learning, and in particular we perform an extensive experimental evaluation on twelve class-incremental methods. We consider several new experimental scenarios, including a comparison of class-incremental methods on multiple large-scale datasets, investigation into small and large domain shifts, and comparison on various network architectures. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | Admin @ si @ MLT2022 | Serial | 3538 | ||
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Author | Arash Akbarinia; C. Alejandro Parraga | ||||
Title | Colour Constancy Beyond the Classical Receptive Field | Type | Journal Article | ||
Year | 2018 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 40 | Issue | 9 | Pages | 2081 - 2094 |
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Abstract | The problem of removing illuminant variations to preserve the colours of objects (colour constancy) has already been solved by the human brain using mechanisms that rely largely on centre-surround computations of local contrast. In this paper we adopt some of these biological solutions described by long known physiological findings into a simple, fully automatic, functional model (termed Adaptive Surround Modulation or ASM). In ASM, the size of a visual neuron's receptive field (RF) as well as the relationship with its surround varies according to the local contrast within the stimulus, which in turn determines the nature of the centre-surround normalisation of cortical neurons higher up in the processing chain. We modelled colour constancy by means of two overlapping asymmetric Gaussian kernels whose sizes are adapted based on the contrast of the surround pixels, resembling the change of RF size. We simulated the contrast-dependent surround modulation by weighting the contribution of each Gaussian according to the centre-surround contrast. In the end, we obtained an estimation of the illuminant from the set of the most activated RFs' outputs. Our results on three single-illuminant and one multi-illuminant benchmark datasets show that ASM is highly competitive against the state-of-the-art and it even outperforms learning-based algorithms in one case. Moreover, the robustness of our model is more tangible if we consider that our results were obtained using the same parameters for all datasets, that is, mimicking how the human visual system operates. These results might provide an insight on how dynamical adaptation mechanisms contribute to make object's colours appear constant to us. | ||||
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Notes | NEUROBIT; 600.068; 600.072 | Approved | no | ||
Call Number | Admin @ si @ AkP2018a | Serial | 2990 | ||
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Author | Sergio Escalera; Jordi Gonzalez; Xavier Baro; Jamie Shotton | ||||
Title | Guest Editor Introduction to the Special Issue on Multimodal Human Pose Recovery and Behavior Analysis | Type | Journal Article | ||
Year | 2016 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 28 | Issue | Pages | 1489 - 1491 | |
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Abstract | The sixteen papers in this special section focus on human pose recovery and behavior analysis (HuPBA). This is one of the most challenging topics in computer vision, pattern analysis, and machine learning. It is of critical importance for application areas that include gaming, computer interaction, human robot interaction, security, commerce, assistive technologies and rehabilitation, sports, sign language recognition, and driver assistance technology, to mention just a few. In essence, HuPBA requires dealing with the articulated nature of the human body, changes in appearance due to clothing, and the inherent problems of clutter scenes, such as background artifacts, occlusions, and illumination changes. These papers represent the most recent research in this field, including new methods considering still images, image sequences, depth data, stereo vision, 3D vision, audio, and IMUs, among others. | ||||
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Notes | HuPBA; ISE;MV; | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2851 | ||
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Author | Ciprian Corneanu; Marc Oliu; Jeffrey F. Cohn; Sergio Escalera | ||||
Title | Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History | Type | Journal Article | ||
Year | 2016 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 28 | Issue | 8 | Pages | 1548-1568 |
Keywords | Facial expression; affect; emotion recognition; RGB; 3D; thermal; multimodal | ||||
Abstract | Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research. | ||||
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Notes | HuPBA;MILAB; | Approved | no | ||
Call Number | Admin @ si @ COC2016 | Serial | 2718 | ||
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Author | G. Lisanti; I. Masi; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Person Re-identification by Iterative Re-weighted Sparse Ranking | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 37 | Issue | 8 | Pages | 1629 - 1642 |
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Abstract | In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second. | ||||
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | LAMP; 601.240; 600.079 | Approved | no | ||
Call Number | Admin @ si @ LMB2015 | Serial | 2557 | ||
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Author | Adriana Romero; Petia Radeva; Carlo Gatta | ||||
Title | Meta-parameter free unsupervised sparse feature learning | Type | Journal Article | ||
Year | 2015 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 37 | Issue | 8 | Pages | 1716-1722 |
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Abstract | We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on CIFAR-10, STL- 10 and UCMerced show that the method achieves the state-of-theart performance, providing discriminative features that generalize well. | ||||
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Notes | MILAB; 600.068; 600.079; 601.160 | Approved | no | ||
Call Number | Admin @ si @ RRG2014b | Serial | 2594 | ||
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Author | Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny | ||||
Title | Word Spotting and Recognition with Embedded Attributes | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 36 | Issue | 12 | Pages | 2552 - 2566 |
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Abstract | This article addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks. | ||||
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | DAG; 600.056; 600.045; 600.061; 602.006; 600.077 | Approved | no | ||
Call Number | Admin @ si @ AGF2014a | Serial | 2483 | ||
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Author | Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez | ||||
Title | Domain Adaptation of Deformable Part-Based Models | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 36 | Issue | 12 | Pages | 2367-2380 |
Keywords | Domain Adaptation; Pedestrian Detection | ||||
Abstract | The accuracy of object classifiers can significantly drop when the training data (source domain) and the application scenario (target domain) have inherent differences. Therefore, adapting the classifiers to the scenario in which they must operate is of paramount importance. We present novel domain adaptation (DA) methods for object detection. As proof of concept, we focus on adapting the state-of-the-art deformable part-based model (DPM) for pedestrian detection. We introduce an adaptive structural SVM (A-SSVM) that adapts a pre-learned classifier between different domains. By taking into account the inherent structure in feature space (e.g., the parts in a DPM), we propose a structure-aware A-SSVM (SA-SSVM). Neither A-SSVM nor SA-SSVM needs to revisit the source-domain training data to perform the adaptation. Rather, a low number of target-domain training examples (e.g., pedestrians) are used. To address the scenario where there are no target-domain annotated samples, we propose a self-adaptive DPM based on a self-paced learning (SPL) strategy and a Gaussian Process Regression (GPR). Two types of adaptation tasks are assessed: from both synthetic pedestrians and general persons (PASCAL VOC) to pedestrians imaged from an on-board camera. Results show that our proposals avoid accuracy drops as high as 15 points when comparing adapted and non-adapted detectors. | ||||
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | ADAS; 600.057; 600.054; 601.217; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ XRV2014b | Serial | 2436 | ||
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Author | Lorenzo Seidenari; Giuseppe Serra; Andrew Bagdanov; Alberto del Bimbo | ||||
Title | Local pyramidal descriptors for image recognition | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 36 | Issue | 5 | Pages | 1033 - 1040 |
Keywords | Object categorization; local features; kernel methods | ||||
Abstract | In this paper we present a novel method to improve the flexibility of descriptor matching for image recognition by using local multiresolution
pyramids in feature space. We propose that image patches be represented at multiple levels of descriptor detail and that these levels be defined in terms of local spatial pooling resolution. Preserving multiple levels of detail in local descriptors is a way of hedging one’s bets on which levels will most relevant for matching during learning and recognition. We introduce the Pyramid SIFT (P-SIFT) descriptor and show that its use in four state-of-the-art image recognition pipelines improves accuracy and yields state-of-the-art results. Our technique is applicable independently of spatial pyramid matching and we show that spatial pyramids can be combined with local pyramids to obtain further improvement.We achieve state-of-the-art results on Caltech-101 (80.1%) and Caltech-256 (52.6%) when compared to other approaches based on SIFT features over intensity images. Our technique is efficient and is extremely easy to integrate into image recognition pipelines. |
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | LAMP; 600.079 | Approved | no | ||
Call Number | Admin @ si @ SSB2014 | Serial | 2524 | ||
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Author | Carlo Gatta; Francesco Ciompi | ||||
Title | Stacked Sequential Scale-Space Taylor Context | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 36 | Issue | 8 | Pages | 1694-1700 |
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Abstract | We analyze sequential image labeling methods that sample the posterior label field in order to gather contextual information. We propose an effective method that extracts local Taylor coefficients from the posterior at different scales. Results show that our proposal outperforms state-of-the-art methods on MSRC-21, CAMVID, eTRIMS8 and KAIST2 data sets. | ||||
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | LAMP; MILAB; 601.160; 600.079 | Approved | no | ||
Call Number | Admin @ si @ GaC2014 | Serial | 2466 | ||
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Author | David Vazquez; Javier Marin; Antonio Lopez; Daniel Ponsa; David Geronimo | ||||
Title | Virtual and Real World Adaptation for Pedestrian Detection | Type | Journal Article | ||
Year | 2014 | Publication | IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
Volume | 36 | Issue | 4 | Pages | 797-809 |
Keywords | Domain Adaptation; Pedestrian Detection | ||||
Abstract | Pedestrian detection is of paramount interest for many applications. Most promising detectors rely on discriminatively learnt classifiers, i.e., trained with annotated samples. However, the annotation step is a human intensive and subjective task worth to be minimized. By using virtual worlds we can automatically obtain precise and rich annotations. Thus, we face the question: can a pedestrian appearance model learnt in realistic virtual worlds work successfully for pedestrian detection in realworld images?. Conducted experiments show that virtual-world based training can provide excellent testing accuracy in real world, but it can also suffer the dataset shift problem as real-world based training does. Accordingly, we have designed a domain adaptation framework, V-AYLA, in which we have tested different techniques to collect a few pedestrian samples from the target domain (real world) and combine them with the many examples of the source domain (virtual world) in order to train a domain adapted pedestrian classifier that will operate in the target domain. V-AYLA reports the same detection accuracy than when training with many human-provided pedestrian annotations and testing with real-world images of the same domain. To the best of our knowledge, this is the first work demonstrating adaptation of virtual and real worlds for developing an object detector. | ||||
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ISSN | 0162-8828 | ISBN | Medium | ||
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Notes | ADAS; 600.057; 600.054; 600.076 | Approved | no | ||
Call Number | ADAS @ adas @ VML2014 | Serial | 2275 | ||
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