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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Andrew Bagdanov; Michael Felsberg; Jorma edit   pdf
url  openurl
  Title (up) 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 Aitor Alvarez-Gila; Adrian Galdran; Estibaliz Garrote; Joost Van de Weijer edit   pdf
url  openurl
  Title (up) Self-supervised blur detection from synthetically blurred scenes Type Journal Article
  Year 2019 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 92 Issue Pages 103804  
  Keywords  
  Abstract Blur detection aims at segmenting the blurred areas of a given image. Recent deep learning-based methods approach this problem by learning an end-to-end mapping between the blurred input and a binary mask representing the localization of its blurred areas. Nevertheless, the effectiveness of such deep models is limited due to the scarcity of datasets annotated in terms of blur segmentation, as blur annotation is labor intensive. In this work, we bypass the need for such annotated datasets for end-to-end learning, and instead rely on object proposals and a model for blur generation in order to produce a dataset of synthetically blurred images. This allows us to perform self-supervised learning over the generated image and ground truth blur mask pairs using CNNs, defining a framework that can be employed in purely self-supervised, weakly supervised or semi-supervised configurations. Interestingly, experimental results of such setups over the largest blur segmentation datasets available show that this approach achieves state of the art results in blur segmentation, even without ever observing any real blurred image.  
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  Notes LAMP; 600.109; 600.120 Approved no  
  Call Number Admin @ si @ AGG2019 Serial 3301  
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Author Lu Yu; Xialei Liu; Joost Van de Weijer edit   pdf
doi  openurl
  Title (up) 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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  Area Expedition Conference  
  Notes LAMP; 600.147; 611.008; Approved no  
  Call Number Admin @ si @ YLW2022 Serial 3745  
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Muhammad Anwer Rao; Michael Felsberg; Carlo Gatta edit   pdf
doi  openurl
  Title (up) Semantic Pyramids for Gender and Action Recognition Type Journal Article
  Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP  
  Volume 23 Issue 8 Pages 3633-3645  
  Keywords  
  Abstract Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.  
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  Series Volume Series Issue Edition  
  ISSN 1057-7149 ISBN Medium  
  Area Expedition Conference  
  Notes CIC; LAMP; 601.160; 600.074; 600.079;MILAB Approved no  
  Call Number Admin @ si @ KWR2014 Serial 2507  
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Author Mikhail Mozerov; Fei Yang; Joost Van de Weijer edit   pdf
doi  openurl
  Title (up) Sparse Data Interpolation Using the Geodesic Distance Affinity Space Type Journal Article
  Year 2019 Publication IEEE Signal Processing Letters Abbreviated Journal SPL  
  Volume 26 Issue 6 Pages 943 - 947  
  Keywords  
  Abstract In this letter, we adapt the geodesic distance-based recursive filter to the sparse data interpolation problem. The proposed technique is general and can be easily applied to any kind of sparse data. We demonstrate its superiority over other interpolation techniques in three experiments for qualitative and quantitative evaluation. In addition, we compare our method with the popular interpolation algorithm presented in the paper on EpicFlow optical flow, which is intuitively motivated by a similar geodesic distance principle. The comparison shows that our algorithm is more accurate and considerably faster than the EpicFlow interpolation technique.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes LAMP; 600.120 Approved no  
  Call Number Admin @ si @ MYW2019 Serial 3261  
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