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| Author | Zhengying Liu; Adrien Pavao; Zhen Xu; Sergio Escalera; Fabio Ferreira; Isabelle Guyon; Sirui Hong; Frank Hutter; Rongrong Ji; Julio C. S. Jacques Junior; Ge Li; Marius Lindauer; Zhipeng Luo; Meysam Madadi; Thomas Nierhoff; Kangning Niu; Chunguang Pan; Danny Stoll; Sebastien Treguer; Jin Wang; Peng Wang; Chenglin Wu; Youcheng Xiong; Arber Zela; Yang Zhang | ||||
| Title | Winning Solutions and Post-Challenge Analyses of the ChaLearn AutoDL Challenge 2019 | Type | Journal Article | ||
| Year | 2021 | Publication  |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 43 | Issue | 9 | Pages | 3108 - 3125 |
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| Abstract | This paper reports the results and post-challenge analyses of ChaLearn's AutoDL challenge series, which helped sorting out a profusion of AutoML solutions for Deep Learning (DL) that had been introduced in a variety of settings, but lacked fair comparisons. All input data modalities (time series, images, videos, text, tabular) were formatted as tensors and all tasks were multi-label classification problems. Code submissions were executed on hidden tasks, with limited time and computational resources, pushing solutions that get results quickly. In this setting, DL methods dominated, though popular Neural Architecture Search (NAS) was impractical. Solutions relied on fine-tuned pre-trained networks, with architectures matching data modality. Post-challenge tests did not reveal improvements beyond the imposed time limit. While no component is particularly original or novel, a high level modular organization emerged featuring a “meta-learner”, “data ingestor”, “model selector”, “model/learner”, and “evaluator”. This modularity enabled ablation studies, which revealed the importance of (off-platform) meta-learning, ensembling, and efficient data management. Experiments on heterogeneous module combinations further confirm the (local) optimality of the winning solutions. Our challenge legacy includes an ever-lasting benchmark (http://autodl.chalearn.org), the open-sourced code of the winners, and a free “AutoDL self-service.” | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ LPX2021 | Serial | 3587 | ||
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| Author | Lei Kang; Pau Riba; Marcal Rusinol; Alicia Fornes; Mauricio Villegas | ||||
| Title | Content and Style Aware Generation of Text-line Images for Handwriting Recognition | Type | Journal Article | ||
| Year | 2021 | Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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| Abstract | Handwritten Text Recognition has achieved an impressive performance in public benchmarks. However, due to the high inter- and intra-class variability between handwriting styles, such recognizers need to be trained using huge volumes of manually labeled training data. To alleviate this labor-consuming problem, synthetic data produced with TrueType fonts has been often used in the training loop to gain volume and augment the handwriting style variability. However, there is a significant style bias between synthetic and real data which hinders the improvement of recognition performance. To deal with such limitations, we propose a generative method for handwritten text-line images, which is conditioned on both visual appearance and textual content. Our method is able to produce long text-line samples with diverse handwriting styles. Once properly trained, our method can also be adapted to new target data by only accessing unlabeled text-line images to mimic handwritten styles and produce images with any textual content. Extensive experiments have been done on making use of the generated samples to boost Handwritten Text Recognition performance. Both qualitative and quantitative results demonstrate that the proposed approach outperforms the current state of the art. | ||||
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| Notes | DAG; 600.140; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ KRR2021 | Serial | 3612 | ||
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| Author | Swathikiran Sudhakaran; Sergio Escalera;Oswald Lanz | ||||
| Title | Learning to Recognize Actions on Objects in Egocentric Video with Attention Dictionaries | Type | Journal Article | ||
| Year | 2021 | Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
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| Abstract | We present EgoACO, a deep neural architecture for video action recognition that learns to pool action-context-object descriptors from frame level features by leveraging the verb-noun structure of action labels in egocentric video datasets. The core component of EgoACO is class activation pooling (CAP), a differentiable pooling operation that combines ideas from bilinear pooling for fine-grained recognition and from feature learning for discriminative localization. CAP uses self-attention with a dictionary of learnable weights to pool from the most relevant feature regions. Through CAP, EgoACO learns to decode object and scene context descriptors from video frame features. For temporal modeling in EgoACO, we design a recurrent version of class activation pooling termed Long Short-Term Attention (LSTA). LSTA extends convolutional gated LSTM with built-in spatial attention and a re-designed output gate. Action, object and context descriptors are fused by a multi-head prediction that accounts for the inter-dependencies between noun-verb-action structured labels in egocentric video datasets. EgoACO features built-in visual explanations, helping learning and interpretation. Results on the two largest egocentric action recognition datasets currently available, EPIC-KITCHENS and EGTEA, show that by explicitly decoding action-context-object descriptors, EgoACO achieves state-of-the-art recognition performance. | ||||
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| Notes | HUPBA; no proj | Approved | no | ||
| Call Number | Admin @ si @ SEL2021 | Serial | 3656 | ||
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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 | 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 | Shiqi Yang; Yaxing Wang; Joost Van de Weijer; Luis Herranz; Shangling Jui; Jian Yang | ||||
| Title | Trust Your Good Friends: Source-Free Domain Adaptation by Reciprocal Neighborhood Clustering | Type | Journal Article | ||
| Year | 2023 | Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 45 | Issue | 12 | Pages | 15883-15895 |
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| Abstract | Domain adaptation (DA) aims to alleviate the domain shift between source domain and target domain. Most DA methods require access to the source data, but often that is not possible (e.g., due to data privacy or intellectual property). In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data. Our method is based on the observation that target data, which might not align with the source domain classifier, still forms clear clusters. We capture this intrinsic structure by defining local affinity of the target data, and encourage label consistency among data with high local affinity. We observe that higher affinity should be assigned to reciprocal neighbors. To aggregate information with more context, we consider expanded neighborhoods with small affinity values. Furthermore, we consider the density around each target sample, which can alleviate the negative impact of potential outliers. In the experimental results we verify that the inherent structure of the target features is an important source of information for domain adaptation. We demonstrate that this local structure can be efficiently captured by considering the local neighbors, the reciprocal neighbors, and the expanded neighborhood. Finally, we achieve state-of-the-art performance on several 2D image and 3D point cloud recognition datasets. | ||||
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| Notes | LAMP; MACO | Approved | no | ||
| Call Number | Admin @ si @ YWW2023 | Serial | 3889 | ||
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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 | Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan C. Moure | ||||
| Title | 3D Perception With Slanted Stixels on GPU | Type | Journal Article | ||
| Year | 2021 | Publication |
IEEE Transactions on Parallel and Distributed Systems | Abbreviated Journal | TPDS |
| Volume | 32 | Issue | 10 | Pages | 2434-2447 |
| Keywords | Daniel Hernandez-Juarez; Antonio Espinosa; David Vazquez; Antonio M. Lopez; Juan C. Moure | ||||
| Abstract | This article presents a GPU-accelerated software design of the recently proposed model of Slanted Stixels, which represents the geometric and semantic information of a scene in a compact and accurate way. We reformulate the measurement depth model to reduce the computational complexity of the algorithm, relying on the confidence of the depth estimation and the identification of invalid values to handle outliers. The proposed massively parallel scheme and data layout for the irregular computation pattern that corresponds to a Dynamic Programming paradigm is described and carefully analyzed in performance terms. Performance is shown to scale gracefully on current generation embedded GPUs. We assess the proposed methods in terms of semantic and geometric accuracy as well as run-time performance on three publicly available benchmark datasets. Our approach achieves real-time performance with high accuracy for 2048 × 1024 image sizes and 4 × 4 Stixel resolution on the low-power embedded GPU of an NVIDIA Tegra Xavier. | ||||
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| Notes | ADAS; 600.124; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ HEV2021 | Serial | 3561 | ||
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| Author | Katerine Diaz; Francesc J. Ferri; W. Diaz | ||||
| Title | Incremental Generalized Discriminative Common Vectors for Image Classification | Type | Journal Article | ||
| Year | 2015 | Publication |
IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | 26 | Issue | 8 | Pages | 1761 - 1775 |
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| Abstract | Subspace-based methods have become popular due to their ability to appropriately represent complex data in such a way that both dimensionality is reduced and discriminativeness is enhanced. Several recent works have concentrated on the discriminative common vector (DCV) method and other closely related algorithms also based on the concept of null space. In this paper, we present a generalized incremental formulation of the DCV methods, which allows the update of a given model by considering the addition of new examples even from unseen classes. Having efficient incremental formulations of well-behaved batch algorithms allows us to conveniently adapt previously trained classifiers without the need of recomputing them from scratch. The proposed generalized incremental method has been empirically validated in different case studies from different application domains (faces, objects, and handwritten digits) considering several different scenarios in which new data are continuously added at different rates starting from an initial model. | ||||
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| ISSN | 2162-237X | ISBN | Medium | ||
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| Notes | ADAS; 600.076 | Approved | no | ||
| Call Number | Admin @ si @ DFD2015 | Serial | 2547 | ||
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| Author | Anjan Dutta; Hichem Sahbi | ||||
| Title | Stochastic Graphlet Embedding | Type | Journal Article | ||
| Year | 2018 | Publication |
IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | 1-14 | ||
| Keywords | Stochastic graphlets; Graph embedding; Graph classification; Graph hashing; Betweenness centrality | ||||
| Abstract | Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives. However, these non-vectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of – explicit/implicit –graph vectorization and embedding. This embedding process should be resilient to intra-class graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding (SGE) that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases. |
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| Notes | DAG; 602.167; 602.168; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ DuS2018 | Serial | 3225 | ||
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| Author | Lu Yu; Xialei Liu; Joost Van de Weijer | ||||
| Title | Self-Training for Class-Incremental Semantic Segmentation | Type | Journal Article | ||
| Year | 2022 | Publication |
IEEE Transactions on Neural Networks and Learning Systems | Abbreviated Journal | TNNLS |
| Volume | Issue | Pages | |||
| Keywords | Class-incremental learning; Self-training; Semantic segmentation. | ||||
| Abstract | In class-incremental semantic segmentation, we have no access to the labeled data of previous tasks. Therefore, when incrementally learning new classes, deep neural networks suffer from catastrophic forgetting of previously learned knowledge. To address this problem, we propose to apply a self-training approach that leverages unlabeled data, which is used for rehearsal of previous knowledge. Specifically, we first learn a temporary model for the current task, and then, pseudo labels for the unlabeled data are computed by fusing information from the old model of the previous task and the current temporary model. In addition, conflict reduction is proposed to resolve the conflicts of pseudo labels generated from both the old and temporary models. We show that maximizing self-entropy can further improve results by smoothing the overconfident predictions. Interestingly, in the experiments, we show that the auxiliary data can be different from the training data and that even general-purpose, but diverse auxiliary data can lead to large performance gains. The experiments demonstrate the state-of-the-art results: obtaining a relative gain of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K compared to previous state-of-the-art methods. | ||||
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| Notes | LAMP; 600.147; 611.008; | Approved | no | ||
| Call Number | Admin @ si @ YLW2022 | Serial | 3745 | ||
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| Author | David Masip; Jordi Vitria | ||||
| Title | Shared Feature Extraction for Nearest Neighbor Face Recognition | Type | Journal | ||
| Year | 2008 | Publication |
IEEE Transactions on Neural Networks | Abbreviated Journal | |
| Volume | 19 | Issue | 4 | Pages | 586–595 |
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| Notes | OR;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ MaV2008 | Serial | 944 | ||
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| Author | A. Diplaros; N. Vlassis; Theo Gevers | ||||
| Title | A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation | Type | Journal | ||
| Year | 2007 | Publication |
IEEE Transactions on Neural Networks | Abbreviated Journal | |
| Volume | 18 | Issue | 3 | Pages | 798-808 |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ DVG2007 | Serial | 947 | ||
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| Author | Koen E.A. van de Sande; Theo Gevers; Cees G.M. Snoek | ||||
| Title | Empowering Visual Categorization with the GPU | Type | Journal Article | ||
| Year | 2011 | Publication |
IEEE Transactions on Multimedia | Abbreviated Journal | TMM |
| Volume | 13 | Issue | 1 | Pages | 60-70 |
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| Abstract | Visual categorization is important to manage large collections of digital images and video, where textual meta-data is often incomplete or simply unavailable. The bag-of-words model has become the most powerful method for visual categorization of images and video. Despite its high accuracy, a severe drawback of this model is its high computational cost. As the trend to increase computational power in newer CPU and GPU architectures is to increase their level of parallelism, exploiting this parallelism becomes an important direction to handle the computational cost of the bag-of-words approach. When optimizing a system based on the bag-of-words approach, the goal is to minimize the time it takes to process batches of images. Additionally, we also consider power usage as an evaluation metric. In this paper, we analyze the bag-of-words model for visual categorization in terms of computational cost and identify two major bottlenecks: the quantization step and the classification step. We address these two bottlenecks by proposing two efficient algorithms for quantization and classification by exploiting the GPU hardware and the CUDA parallel programming model. The algorithms are designed to (1) keep categorization accuracy intact, (2) decompose the problem and (3) give the same numerical results. In the experiments on large scale datasets it is shown that, by using a parallel implementation on the Geforce GTX260 GPU, classifying unseen images is 4.8 times faster than a quad-core CPU version on the Core i7 920, while giving the exact same numerical results. In addition, we show how the algorithms can be generalized to other applications, such as text retrieval and video retrieval. Moreover, when the obtained speedup is used to process extra video frames in a video retrieval benchmark, the accuracy of visual categorization is improved by 29%. | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ SGS2011b | Serial | 1729 | ||
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