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Author | Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma | ||||
Title | Scale coding bag of deep features for human attribute and action recognition | Type | Journal Article | ||
Year | 2018 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 29 | Issue | 1 | Pages | 55-71 |
Keywords | Action recognition; Attribute recognition; Bag of deep features | ||||
Abstract | Most approaches to human attribute and action recognition in still images are based on image representation in which multi-scale local features are pooled across scale into a single, scale-invariant encoding. Both in bag-of-words and the recently popular representations based on convolutional neural networks, local features are computed at multiple scales. However, these multi-scale convolutional features are pooled into a single scale-invariant representation. We argue that entirely scale-invariant image representations are sub-optimal and investigate approaches to scale coding within a bag of deep features framework. Our approach encodes multi-scale information explicitly during the image encoding stage. We propose two strategies to encode multi-scale information explicitly in the final image representation. We validate our two scale coding techniques on five datasets: Willow, PASCAL VOC 2010, PASCAL VOC 2012, Stanford-40 and Human Attributes (HAT-27). On all datasets, the proposed scale coding approaches outperform both the scale-invariant method and the standard deep features of the same network. Further, combining our scale coding approaches with standard deep features leads to consistent improvement over the state of the art. | ||||
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Notes | LAMP; 600.068; 600.079; 600.106; 600.120 | Approved | no | ||
Call Number | Admin @ si @ KWR2018 | Serial | 3107 | ||
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Author | F. Javier Sanchez; Jorge Bernal; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Gloria Fernandez Esparrach | ||||
Title | Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos | Type | Journal Article | ||
Year | 2017 | Publication | Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | Issue | Pages | 1-20 | ||
Keywords | Specular highlights; bright spot regions segmentation; region classification; colonoscopy | ||||
Abstract | A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance dening specular
highlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classication of bright spot regions. The former denes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classication stage performed by a linear SVM classier using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions being closer to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology. |
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Notes | MV; 600.096; 600.175 | Approved | no | ||
Call Number | Admin @ si @ SBS2017 | Serial | 2975 | ||
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Author | Mariella Dimiccoli; Jean-Pascal Jacob; Lionel Moisan | ||||
Title | Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach | Type | Journal Article | ||
Year | 2016 | Publication | Journal of Machine Vision and Applications | Abbreviated Journal | MVAP |
Volume | 27 | Issue | Pages | 511-527 | |
Keywords | particle detection; particle tracking; a-contrario approach; time-lapse fluorescence imaging | ||||
Abstract | In this work, we propose a probabilistic approach for the detection and the
tracking of particles on biological images. In presence of very noised and poor quality data, particles and trajectories can be characterized by an a-contrario model, that estimates the probability of observing the structures of interest in random data. This approach, first introduced in the modeling of human visual perception and then successfully applied in many image processing tasks, leads to algorithms that do not require a previous learning stage, nor a tedious parameter tuning and are very robust to noise. Comparative evaluations against a well established baseline show that the proposed approach outperforms the state of the art. |
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Notes | MILAB; | Approved | no | ||
Call Number | Admin @ si @ DJM2016 | Serial | 2735 | ||
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Author | Sergio Escalera; Jordi Gonzalez; Hugo Jair Escalante; Xavier Baro; Isabelle Guyon | ||||
Title | Looking at People Special Issue | Type | Journal Article | ||
Year | 2018 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
Volume | 126 | Issue | 2-4 | Pages | 141-143 |
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Notes | HUPBA; ISE; 600.119 | Approved | no | ||
Call Number | Admin @ si @ EGJ2018 | Serial | 3093 | ||
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Author | Alejandro Cartas; Juan Marin; Petia Radeva; Mariella Dimiccoli | ||||
Title | Batch-based activity recognition from egocentric photo-streams revisited | Type | Journal Article | ||
Year | 2018 | Publication | Pattern Analysis and Applications | Abbreviated Journal | PAA |
Volume | 21 | Issue | 4 | Pages | 953–965 |
Keywords | Egocentric vision; Lifelogging; Activity recognition; Deep learning; Recurrent neural networks | ||||
Abstract | Wearable cameras can gather large amounts of image data that provide rich visual information about the daily activities of the wearer. Motivated by the large number of health applications that could be enabled by the automatic recognition of daily activities, such as lifestyle characterization for habit improvement, context-aware personal assistance and tele-rehabilitation services, we propose a system to classify 21 daily activities from photo-streams acquired by a wearable photo-camera. Our approach combines the advantages of a late fusion ensemble strategy relying on convolutional neural networks at image level with the ability of recurrent neural networks to account for the temporal evolution of high-level features in photo-streams without relying on event boundaries. The proposed batch-based approach achieved an overall accuracy of 89.85%, outperforming state-of-the-art end-to-end methodologies. These results were achieved on a dataset consists of 44,902 egocentric pictures from three persons captured during 26 days in average. | ||||
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Notes | MILAB; no proj | Approved | no | ||
Call Number | Admin @ si @ CMR2018 | Serial | 3186 | ||
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Author | Domicele Jonauskaite; Nele Dael; C. Alejandro Parraga; Laetitia Chevre; Alejandro Garcia Sanchez; Christine Mohr | ||||
Title | Stripping #The Dress: The importance of contextual information on inter-individual differences in colour perception | Type | Journal Article | ||
Year | 2018 | Publication | Psychological Research | Abbreviated Journal | PSYCHO R |
Volume | Issue | Pages | 1-15 | ||
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Abstract | In 2015, a picture of a Dress (henceforth the Dress) triggered popular and scientific interest; some reported seeing the Dress in white and gold (W&G) and others in blue and black (B&B). We aimed to describe the phenomenon and investigate the role of contextualization. Few days after the Dress had appeared on the Internet, we projected it to 240 students on two large screens in the classroom. Participants reported seeing the Dress in B&B (48%), W&G (38%), or blue and brown (B&Br; 7%). Amongst numerous socio-demographic variables, we only observed that W&G viewers were most likely to have always seen the Dress as W&G. In the laboratory, we tested how much contextual information is necessary for the phenomenon to occur. Fifty-seven participants selected colours most precisely matching predominant colours of parts or the full Dress. We presented, in this order, small squares (a), vertical strips (b), and the full Dress (c). We found that (1) B&B, B&Br, and W&G viewers had selected colours differing in lightness and chroma levels for contextualized images only (b, c conditions) and hue for fully contextualized condition only (c) and (2) B&B viewers selected colours most closely matching displayed colours of the Dress. Thus, the Dress phenomenon emerges due to inter-individual differences in subjectively perceived lightness, chroma, and hue, at least when all aspects of the picture need to be integrated. Our results support the previous conclusions that contextual information is key to colour perception; it should be important to understand how this actually happens. | ||||
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Notes | NEUROBIT; no proj | Approved | no | ||
Call Number | Admin @ si @ JDP2018 | Serial | 3149 | ||
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Author | Alex Gomez-Villa; Adrian Martin; Javier Vazquez; Marcelo Bertalmio; Jesus Malo | ||||
Title | On the synthesis of visual illusions using deep generative models | Type | Journal Article | ||
Year | 2022 | Publication | Journal of Vision | Abbreviated Journal | JOV |
Volume | 22(8) | Issue | 2 | Pages | 1-18 |
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Abstract | Visual illusions expand our understanding of the visual system by imposing constraints in the models in two different ways: i) visual illusions for humans should induce equivalent illusions in the model, and ii) illusions synthesized from the model should be compelling for human viewers too. These constraints are alternative strategies to find good vision models. Following the first research strategy, recent studies have shown that artificial neural network architectures also have human-like illusory percepts when stimulated with classical hand-crafted stimuli designed to fool humans. In this work we focus on the second (less explored) strategy: we propose a framework to synthesize new visual illusions using the optimization abilities of current automatic differentiation techniques. The proposed framework can be used with classical vision models as well as with more recent artificial neural network architectures. This framework, validated by psychophysical experiments, can be used to study the difference between a vision model and the actual human perception and to optimize the vision model to decrease this difference. | ||||
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Notes | LAMP; 600.161; 611.007 | Approved | no | ||
Call Number | Admin @ si @ GMV2022 | Serial | 3682 | ||
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Author | Trevor Canham; Javier Vazquez; Elise Mathieu; Marcelo Bertalmío | ||||
Title | Matching visual induction effects on screens of different size | Type | Journal Article | ||
Year | 2021 | Publication | Journal of Vision | Abbreviated Journal | JOV |
Volume | 21 | Issue | 6(10) | Pages | 1-22 |
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Abstract | In the film industry, the same movie is expected to be watched on displays of vastly different sizes, from cinema screens to mobile phones. But visual induction, the perceptual phenomenon by which the appearance of a scene region is affected by its surroundings, will be different for the same image shown on two displays of different dimensions. This phenomenon presents a practical challenge for the preservation of the artistic intentions of filmmakers, because it can lead to shifts in image appearance between viewing destinations. In this work, we show that a neural field model based on the efficient representation principle is able to predict induction effects and how, by regularizing its associated energy functional, the model is still able to represent induction but is now invertible. From this finding, we propose a method to preprocess an image in a screen–size dependent way so that its perception, in terms of visual induction, may remain constant across displays of different size. The potential of the method is demonstrated through psychophysical experiments on synthetic images and qualitative examples on natural images. | ||||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ CVM2021 | Serial | 3595 | ||
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Author | David Berga; C. Wloka; JK. Tsotsos | ||||
Title | Modeling task influences for saccade sequence and visual relevance prediction | Type | Journal Article | ||
Year | 2019 | Publication | Journal of Vision | Abbreviated Journal | JV |
Volume | 19 | Issue | 10 | Pages | 106c-106c |
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Abstract | Previous work from Wloka et al. (2017) presented the Selective Tuning Attentive Reference model Fixation Controller (STAR-FC), an active vision model for saccade prediction. Although the model is able to efficiently predict saccades during free-viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns (Yarbus, 1967). These factors are considered in previous Selective Tuning architectures (Tsotsos and Kruijne, 2014)(Tsotsos, Kotseruba and Wloka, 2016)(Rosenfeld, Biparva & Tsotsos 2017), proposing a way to combine bottom-up and top-down contributions to fixation and saccade programming. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-term memory in combination with stimulus-driven eye movement neuronal correlates. Initial theories and models of these influences include (Rao, Zelinsky, Hayhoe and Ballard, 2002)(Navalpakkam and Itti, 2005)(Huang and Pashler, 2007) and show distinct ways to process the task requirements in combination with bottom-up attention. In this study we extend the STAR-FC with novel computational definitions of Long-Term Memory, Visual Task Executive and a Task Relevance Map. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memory model by processing a hierarchy of visual features learned from salient object detection datasets. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting relevance maps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions. | ||||
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Notes | NEUROBIT; 600.128; 600.120 | Approved | no | ||
Call Number | Admin @ si @ BWT2019 | Serial | 3308 | ||
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Author | Hans Stadthagen-Gonzalez; M. Carmen Parafita; C. Alejandro Parraga; Markus F. Damian | ||||
Title | Testing alternative theoretical accounts of code-switching: Insights from comparative judgments of adjective noun order | Type | Journal Article | ||
Year | 2019 | Publication | International journal of bilingualism: interdisciplinary studies of multilingual behaviour | Abbreviated Journal | IJB |
Volume | 23 | Issue | 1 | Pages | 200-220 |
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Abstract | Objectives:
Spanish and English contrast in adjective–noun word order: for example, brown dress (English) vs. vestido marrón (‘dress brown’, Spanish). According to the Matrix Language model (MLF) word order in code-switched sentences must be compatible with the word order of the matrix language, but working within the minimalist program (MP), Cantone and MacSwan arrived at the descriptive generalization that the position of the noun phrase relative to the adjective is determined by the adjective’s language. Our aim is to evaluate the predictions derived from these two models regarding adjective–noun order in Spanish–English code-switched sentences. Methodology: We contrasted the predictions from both models regarding the acceptability of code-switched sentences with different adjective–noun orders that were compatible with the MP, the MLF, both, or none. Acceptability was assessed in Experiment 1 with a 5-point Likert and in Experiment 2 with a 2-Alternative Forced Choice (2AFC) task. |
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Notes | NEUROBIT; no menciona | Approved | no | ||
Call Number | Admin @ si @ SPP2019 | Serial | 3242 | ||
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Author | Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell | ||||
Title | Segmentation of Distal Airways using Structural Analysis | Type | Journal Article | ||
Year | 2019 | Publication | PloS one | Abbreviated Journal | Plos |
Volume | 14 | Issue | 12 | Pages | |
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Abstract | Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution. | ||||
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Notes | IAM; 600.139; 600.145 | Approved | no | ||
Call Number | Admin @ si @ GSB2019 | Serial | 3357 | ||
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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 | Diego Velazquez; Josep M. Gonfaus; Pau Rodriguez; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez | ||||
Title | Logo Detection With No Priors | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Access | Abbreviated Journal | ACCESS |
Volume | 9 | Issue | Pages | 106998-107011 | |
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Abstract | In recent years, top referred methods on object detection like R-CNN have implemented this task as a combination of proposal region generation and supervised classification on the proposed bounding boxes. Although this pipeline has achieved state-of-the-art results in multiple datasets, it has inherent limitations that make object detection a very complex and inefficient task in computational terms. Instead of considering this standard strategy, in this paper we enhance Detection Transformers (DETR) which tackles object detection as a set-prediction problem directly in an end-to-end fully differentiable pipeline without requiring priors. In particular, we incorporate Feature Pyramids (FP) to the DETR architecture and demonstrate the effectiveness of the resulting DETR-FP approach on improving logo detection results thanks to the improved detection of small logos. So, without requiring any domain specific prior to be fed to the model, DETR-FP obtains competitive results on the OpenLogo and MS-COCO datasets offering a relative improvement of up to 30%, when compared to a Faster R-CNN baseline which strongly depends on hand-designed priors. | ||||
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Notes | ISE | Approved | no | ||
Call Number | Admin @ si @ VGR2021 | Serial | 3664 | ||
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Author | Victor M. Campello; Polyxeni Gkontra; Cristian Izquierdo; Carlos Martin-Isla; Alireza Sojoudi; Peter M. Full; Klaus Maier-Hein; Yao Zhang; Zhiqiang He; Jun Ma; Mario Parreno; Alberto Albiol; Fanwei Kong; Shawn C. Shadden; Jorge Corral Acero; Vaanathi Sundaresan; Mina Saber; Mustafa Elattar; Hongwei Li; Bjoern Menze; Firas Khader; Christoph Haarburger; Cian M. Scannell; Mitko Veta; Adam Carscadden; Kumaradevan Punithakumar; Xiao Liu; Sotirios A. Tsaftaris; Xiaoqiong Huang; Xin Yang; Lei Li; Xiahai Zhuang; David Vilades; Martin L. Descalzo; Andrea Guala; Lucia La Mura; Matthias G. Friedrich; Ria Garg; Julie Lebel; Filipe Henriques; Mahir Karakas; Ersin Cavus; Steffen E. Petersen; Sergio Escalera; Santiago Segui; Jose F. Rodriguez Palomares; Karim Lekadir | ||||
Title | Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Transactions on Medical Imaging | Abbreviated Journal | TMI |
Volume | 40 | Issue | 12 | Pages | 3543-3554 |
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Abstract | The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. | ||||
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Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ CGI2021 | Serial | 3653 | ||
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Author | Shiqi Yang; Kai Wang; Luis Herranz; Joost Van de Weijer | ||||
Title | On Implicit Attribute Localization for Generalized Zero-Shot Learning | Type | Journal Article | ||
Year | 2021 | Publication | IEEE Signal Processing Letters | Abbreviated Journal | |
Volume | 28 | Issue | Pages | 872 - 876 | |
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Abstract | Zero-shot learning (ZSL) aims to discriminate images from unseen classes by exploiting relations to seen classes via their attribute-based descriptions. Since attributes are often related to specific parts of objects, many recent works focus on discovering discriminative regions. However, these methods usually require additional complex part detection modules or attention mechanisms. In this paper, 1) we show that common ZSL backbones (without explicit attention nor part detection) can implicitly localize attributes, yet this property is not exploited. 2) Exploiting it, we then propose SELAR, a simple method that further encourages attribute localization, surprisingly achieving very competitive generalized ZSL (GZSL) performance when compared with more complex state-of-the-art methods. Our findings provide useful insight for designing future GZSL methods, and SELAR provides an easy to implement yet strong baseline. | ||||
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Notes | LAMP; 600.120 | Approved | no | ||
Call Number | YWH2021 | Serial | 3563 | ||
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