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Huamin Ren, Weifeng Liu, Soren Ingvor Olsen, Sergio Escalera, & Thomas B. Moeslund. (2015). Unsupervised Behavior-Specific Dictionary Learning for Abnormal Event Detection. In 26th British Machine Vision Conference.
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Daniel Hernandez, Lukas Schneider, Antonio Espinosa, David Vazquez, Antonio Lopez, Uwe Franke, et al. (2017). Slanted Stixels: Representing San Francisco's Steepest Streets}. In 28th British Machine Vision Conference.
Abstract: In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced that uses an extremely efficient over-segmentation. In doing so, the computational complexity of the Stixel inference algorithm is reduced significantly, achieving real-time computation capabilities with only a slight drop in accuracy. We evaluate the proposed approach in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
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Vassileios Balntas, Edgar Riba, Daniel Ponsa, & Krystian Mikolajczyk. (2016). Learning local feature descriptors with triplets and shallow convolutional neural networks. In 27th British Machine Vision Conference.
Abstract: It has recently been demonstrated that local feature descriptors based on convolutional neural networks (CNN) can significantly improve the matching performance. Previous work on learning such descriptors has focused on exploiting pairs of positive and negative patches to learn discriminative CNN representations. In this work, we propose to utilize triplets of training samples, together with in-triplet mining of hard negatives.
We show that our method achieves state of the art results, without the computational overhead typically associated with mining of negatives and with lower complexity of the network architecture. We compare our approach to recently introduced convolutional local feature descriptors, and demonstrate the advantages of the proposed methods in terms of performance and speed. We also examine different loss functions associated with triplets.
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Arash Akbarinia, & C. Alejandro Parraga. (2016). Biologically plausible boundary detection. In 27th British Machine Vision Conference.
Abstract: Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.
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Arash Akbarinia, Raquel Gil Rodriguez, & C. Alejandro Parraga. (2017). Colour Constancy: Biologically-inspired Contrast Variant Pooling Mechanism. In 28th British Machine Vision Conference.
Abstract: Pooling is a ubiquitous operation in image processing algorithms that allows for higher-level processes to collect relevant low-level features from a region of interest. Currently, max-pooling is one of the most commonly used operators in the computational literature. However, it can lack robustness to outliers due to the fact that it relies merely on the peak of a function. Pooling mechanisms are also present in the primate visual cortex where neurons of higher cortical areas pool signals from lower ones. The receptive fields of these neurons have been shown to vary according to the contrast by aggregating signals over a larger region in the presence of low contrast stimuli. We hypothesise that this contrast-variant-pooling mechanism can address some of the shortcomings of maxpooling. We modelled this contrast variation through a histogram clipping in which the percentage of pooled signal is inversely proportional to the local contrast of an image. We tested our hypothesis by applying it to the phenomenon of colour constancy where a number of popular algorithms utilise a max-pooling step (e.g. White-Patch, Grey-Edge and Double-Opponency). For each of these methods, we investigated the consequences of replacing their original max-pooling by the proposed contrast-variant-pooling. Our experiments on three colour constancy benchmark datasets suggest that previous results can significantly improve by adopting a contrast-variant-pooling mechanism.
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Rada Deeb, Damien Muselet, Mathieu Hebert, Alain Tremeau, & Joost Van de Weijer. (2017). 3D color charts for camera spectral sensitivity estimation. In 28th British Machine Vision Conference.
Abstract: Estimating spectral data such as camera sensor responses or illuminant spectral power distribution from raw RGB camera outputs is crucial in many computer vision applications.
Usually, 2D color charts with various patches of known spectral reflectance are
used as reference for such purpose. Deducing n-D spectral data (n»3) from 3D RGB inputs is an ill-posed problem that requires a high number of inputs. Unfortunately, most of the natural color surfaces have spectral reflectances that are well described by low-dimensional linear models, i.e. each spectral reflectance can be approximated by a weighted sum of the others. It has been shown that adding patches to color charts does not help in practice, because the information they add is redundant with the information provided by the first set of patches. In this paper, we propose to use spectral data of
higher dimensionality by using 3D color charts that create inter-reflections between the surfaces. These inter-reflections produce multiplications between natural spectral curves and so provide non-linear spectral curves. We show that such data provide enough information for accurate spectral data estimation.
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Marc Masana, Idoia Ruiz, Joan Serrat, Joost Van de Weijer, & Antonio Lopez. (2018). Metric Learning for Novelty and Anomaly Detection. In 29th British Machine Vision Conference.
Abstract: When neural networks process images which do not resemble the distribution seen during training, so called out-of-distribution images, they often make wrong predictions, and do so too confidently. The capability to detect out-of-distribution images is therefore crucial for many real-world applications. We divide out-of-distribution detection between novelty detection ---images of classes which are not in the training set but are related to those---, and anomaly detection ---images with classes which are unrelated to the training set. By related we mean they contain the same type of objects, like digits in MNIST and SVHN. Most existing work has focused on anomaly detection, and has addressed this problem considering networks trained with the cross-entropy loss. Differently from them, we propose to use metric learning which does not have the drawback of the softmax layer (inherent to cross-entropy methods), which forces the network to divide its prediction power over the learned classes. We perform extensive experiments and evaluate both novelty and anomaly detection, even in a relevant application such as traffic sign recognition, obtaining comparable or better results than previous works.
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Cristina Palmero, Javier Selva, Mohammad Ali Bagheri, & Sergio Escalera. (2018). Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues. In 29th British Machine Vision Conference.
Abstract: Gaze behavior is an important non-verbal cue in social signal processing and humancomputer interaction. In this paper, we tackle the problem of person- and head poseindependent 3D gaze estimation from remote cameras, using a multi-modal recurrent convolutional neural network (CNN). We propose to combine face, eyes region, and face landmarks as individual streams in a CNN to estimate gaze in still images. Then, we exploit the dynamic nature of gaze by feeding the learned features of all the frames in a sequence to a many-to-one recurrent module that predicts the 3D gaze vector of the last frame. Our multi-modal static solution is evaluated on a wide range of head poses and gaze directions, achieving a significant improvement of 14.6% over the state of the art on
EYEDIAP dataset, further improved by 4% when the temporal modality is included.
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Bojana Gajic, Ariel Amato, Ramon Baldrich, & Carlo Gatta. (2019). Bag of Negatives for Siamese Architectures. In 30th British Machine Vision Conference.
Abstract: Training a Siamese architecture for re-identification with a large number of identities is a challenging task due to the difficulty of finding relevant negative samples efficiently. In this work we present Bag of Negatives (BoN), a method for accelerated and improved training of Siamese networks that scales well on datasets with a very large number of identities. BoN is an efficient and loss-independent method, able to select a bag of high quality negatives, based on a novel online hashing strategy.
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Sagnik Das, Hassan Ahmed Sial, Ke Ma, Ramon Baldrich, Maria Vanrell, & Dimitris Samaras. (2020). Intrinsic Decomposition of Document Images In-the-Wild. In 31st British Machine Vision Conference.
Abstract: Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised
methods on real data are impossible due to the large amount of data needed. Hence, the
current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW.
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Kai Wang, Fei Yang, & Joost Van de Weijer. (2022). Attention Distillation: self-supervised vision transformer students need more guidance. In 33rd British Machine Vision Conference.
Abstract: Self-supervised learning has been widely applied to train high-quality vision transformers. Unleashing their excellent performance on memory and compute constraint devices is therefore an important research topic. However, how to distill knowledge from one self-supervised ViT to another has not yet been explored. Moreover, the existing self-supervised knowledge distillation (SSKD) methods focus on ConvNet based architectures are suboptimal for ViT knowledge distillation. In this paper, we study knowledge distillation of self-supervised vision transformers (ViT-SSKD). We show that directly distilling information from the crucial attention mechanism from teacher to student can significantly narrow the performance gap between both. In experiments on ImageNet-Subset and ImageNet-1K, we show that our method AttnDistill outperforms existing self-supervised knowledge distillation (SSKD) methods and achieves state-of-the-art k-NN accuracy compared with self-supervised learning (SSL) methods learning from scratch (with the ViT-S model). We are also the first to apply the tiny ViT-T model on self-supervised learning. Moreover, AttnDistill is independent of self-supervised learning algorithms, it can be adapted to ViT based SSL methods to improve the performance in future research.
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Kai Wang, Chenshen Wu, Andrew Bagdanov, Xialei Liu, Shiqi Yang, Shangling Jui, et al. (2022). Positive Pair Distillation Considered Harmful: Continual Meta Metric Learning for Lifelong Object Re-Identification. In 33rd British Machine Vision Conference.
Abstract: Lifelong object re-identification incrementally learns from a stream of re-identification tasks. The objective is to learn a representation that can be applied to all tasks and that generalizes to previously unseen re-identification tasks. The main challenge is that at inference time the representation must generalize to previously unseen identities. To address this problem, we apply continual meta metric learning to lifelong object re-identification. To prevent forgetting of previous tasks, we use knowledge distillation and explore the roles of positive and negative pairs. Based on our observation that the distillation and metric losses are antagonistic, we propose to remove positive pairs from distillation to robustify model updates. Our method, called Distillation without Positive Pairs (DwoPP), is evaluated on extensive intra-domain experiments on person and vehicle re-identification datasets, as well as inter-domain experiments on the LReID benchmark. Our experiments demonstrate that DwoPP significantly outperforms the state-of-the-art.
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Marçal Rusiñol, Lluis Gomez, A. Landman, M. Silva Constenla, & Dimosthenis Karatzas. (2019). Automatic Structured Text Reading for License Plates and Utility Meters. In BMVC Workshop on Visual Artificial Intelligence and Entrepreneurship.
Abstract: Reading text in images has attracted interest from computer vision researchers for
many years. Our technology focuses on the extraction of structured text – such as serial
numbers, machine readings, product codes, etc. – so that it is able to center its attention just on the relevant textual elements. It is conceived to work in an end-to-end fashion, bypassing any explicit text segmentation stage. In this paper we present two different industrial use cases where we have applied our automatic structured text reading technology. In the first one, we demonstrate an outstanding performance when reading license plates compared to the current state of the art. In the second one, we present results on our solution for reading utility meters. The technology is commercialized by a recently created spin-off company, and both solutions are at different stages of integration with final clients.
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Jaume Amores, N. Sebe, & Petia Radeva. (2007). Class-Specific Binaryy Correlograms for Object Recognition. In British Machine Vision Conference.
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Bogdan Raducanu, & Jordi Vitria. (2007). Incremental Subspace Learning for Cognitive Visual Processes. In Advances in Brain, Vision and Artificial Intelligence, 2nd International Symposium (Vol. 4729, 214–223). LNCS.
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