Learning detectors quickly using structured covariance matrices

Computer vision is increasingly becoming interested in the rapid estimation of object detectors. Canonical hard negative mining strategies are slow as they require multiple passes of the large negative training set. Recent work has demonstrated that if the distribution of negative examples is assumed to be stationary, then Linear Discriminant Analysis (LDA) can learn comparable detectors without ever revisiting the negative set. Even with this insight, however, the time to learn a single object detector can still be on the order of tens of seconds on a modern desktop computer. This paper proposes to leverage the resulting structured covariance matrix to obtain detectors with identical performance in orders of magnitude less time and memory. We elucidate an important connection to the correlation filter literature, demonstrating that these can also be trained without ever revisiting the negative set

Staple: Complementary Learners for Real-Time Tracking

Correlation Filter-based trackers have recently achieved excellent performance, showing great robustness to challenging situations exhibiting motion blur and illumination changes. However, since the model that they learn depends strongly on the spatial layout of the tracked object, they are notoriously sensitive to deformation. Models based on colour statistics have complementary traits: they cope well with variation in shape, but suffer when illumination is not consistent throughout a sequence. Moreover, colour distributions alone can be insufficiently discriminative. In this paper, we show that a simple tracker combining complementary cues in a ridge regression framework can operate faster than 80 FPS and outperform not only all entries in the popular VOT14 competition, but also recent and far more sophisticated trackers according to multiple benchmarks.Comment: To appear in CVPR 201

End-to-end representation learning for Correlation Filter based tracking

The Correlation Filter is an algorithm that trains a linear template to discriminate between images and their translations. It is well suited to object tracking because its formulation in the Fourier domain provides a fast solution, enabling the detector to be re-trained once per frame. Previous works that use the Correlation Filter, however, have adopted features that were either manually designed or trained for a different task. This work is the first to overcome this limitation by interpreting the Correlation Filter learner, which has a closed-form solution, as a differentiable layer in a deep neural network. This enables learning deep features that are tightly coupled to the Correlation Filter. Experiments illustrate that our method has the important practical benefit of allowing lightweight architectures to achieve state-of-the-art performance at high framerates.Comment: To appear at CVPR 201

Learning feed-forward one-shot learners

One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.Comment: The first three authors contributed equally, and are listed in alphabetical orde

On progressive sharpening, flat minima and generalisation

We present a new approach to understanding the relationship between loss curvature and input-output model behaviour in deep learning. Specifically, we use existing empirical analyses of the spectrum of deep network loss Hessians to ground an ansatz tying together the loss Hessian and the input-output Jacobian of a deep neural network over training samples throughout training. We then prove a series of theoretical results which quantify the degree to which the input-output Jacobian of a model approximates its Lipschitz norm over a data distribution, and deduce a novel generalisation bound in terms of the empirical Jacobian. We use our ansatz, together with our theoretical results, to give a new account of the recently observed progressive sharpening phenomenon, as well as the generalisation properties of flat minima. Experimental evidence is provided to validate our claims

A NEW PROCEDURE TO ISOLATE BRAIN MITOCHONDRIA FROM HUMAN CORTEX AND ITS APPLICATION FOR LIPID ANALYSIS IN PHYSIOLOGICAL AGING

Many studies have revealed the importance of mitochondria as cellular organelles decisively involved in the onset or progression of neurodegenerative diseases, whose main risk factor is aging. Current protocols for brain mitochondria isolation have been developed to preserve viability, sacrificing the purity that is required to perform high-throughput biochemical analyses. My Phd project focused on the development of a new procedure to obtain a highly pure mitochondrial fraction starting from post mortem frozen tissues of human brain cortex of healthy subjects. The evaluation of mitochondrial enrichment and other cellular contaminants has been performed through different enzyme assays, western blot analyses and transmission electron microscopy. These validation experiments demonstrated the purity of mitochondria and their integrity, as well as the preservation of mitochondria-associated membranes. The brain aging process is allegedly responsible for chemical modification of lipids and changes in the lipid composition of cell membranes. In this scenario, there are no previous studies on human brain mitochondria lipids. Thus, this new method has been applied to investigate lipid composition of pure mitochondria by means of thin layer chromatography. Furthermore, we investigated if there were aging related changes in the lipid composition of these organelles essentials to cell life and death, since that could produce an impairment of the membrane function

Devon: Deformable Volume Network for Learning Optical Flow

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions. Despite their impressive results, it is known that there are two problems with the approach. First, the multi-resolution estimation of optical flow fails in situations where small objects move fast. Second, warping creates artifacts when occlusion or dis-occlusion happens. In this paper, we propose a new neural network module, Deformable Cost Volume, which alleviates the two problems. Based on this module, we designed the Deformable Volume Network (Devon) which can estimate multi-scale optical flow in a single high resolution. Experiments show Devon is more suitable in handling small objects moving fast and achieves comparable results to the state-of-the-art methods in public benchmarks

Long-Term Visual Object Tracking Benchmark

We propose a new long video dataset (called Track Long and Prosper - TLP) and benchmark for single object tracking. The dataset consists of 50 HD videos from real world scenarios, encompassing a duration of over 400 minutes (676K frames), making it more than 20 folds larger in average duration per sequence and more than 8 folds larger in terms of total covered duration, as compared to existing generic datasets for visual tracking. The proposed dataset paves a way to suitably assess long term tracking performance and train better deep learning architectures (avoiding/reducing augmentation, which may not reflect real world behaviour). We benchmark the dataset on 17 state of the art trackers and rank them according to tracking accuracy and run time speeds. We further present thorough qualitative and quantitative evaluation highlighting the importance of long term aspect of tracking. Our most interesting observations are (a) existing short sequence benchmarks fail to bring out the inherent differences in tracking algorithms which widen up while tracking on long sequences and (b) the accuracy of trackers abruptly drops on challenging long sequences, suggesting the potential need of research efforts in the direction of long-term tracking.Comment: ACCV 2018 (Oral

Italian multicenter survey to evaluate the opinion of patients and their reference clinicians on the "tolerance" to targeted therapies already available for non-small cell lung cancer treatment in daily clinical practice

INTRODUCTION: The introduction of targeted therapies in non-small cell lung cancer (NSCLC) treatment has led to emerging toxicities, whose management and impact on quality-of-life (QoL) is not clearly defined. Aim of this Italian multicenter survey was to highlight any discrepancy between patients’ and clinicians’ perception of such toxicities in order to improve their management. METHODS: From October 2013 to April 2014, 133 NSCLC advanced patients, treated with targeted therapies, were consecutively enrolled to assess toxicities and QoL with dedicated questionnaires. One hundred and sixteen patients were included in the final analysis, having attended three consecutive evaluations (T0, T1, T2), starting at least 15 days after the biological treatment. The survey required monthly compilation of both physicians and patients’ questionnaires, basing adverse event evaluation on CTCAE version 4.0. RESULTS: Most of the patients received either an EGFR-TKI or an anaplastic lymphoma kinase (ALK) inhibitor as targeted therapy (84.5% and 13.8%, respectively). At every checkpoint (T0, T1, T2) a significant difference in terms of perception of targeted therapies-related toxicities of any type and grade was described (P value =0.0001 in all cases). This difference was more pronounced for skin toxicity, fatigue and diarrhea. Furthermore, also the assessment of QoL revealed contrasting data between patients and clinicians, mainly QoL reported as good by the majority of patients and daily activities considered as slightly influenced by targeted therapies. CONCLUSIONS: In our knowledge, this is the first prospective survey in patients and doctors specifically designed for targeted therapies in advanced NSCLC. The results show an underestimation of toxicities by clinicians when compared with patients, the difference being greater for adverse events more strongly associated with daily life and QoL. Further studies are needed to confirm our first results. The discrepancy in perception of targeted therapies-related toxicities should be a result from which to start thinking about a new approach in their management