OnionNet: Sharing Features in Cascaded Deep Classifiers

The focus of our work is speeding up evaluation of deep neural networks in retrieval scenarios, where conventional architectures may spend too much time on negative examples. We propose to replace a monolithic network with our novel cascade of feature-sharing deep classifiers, called OnionNet, where subsequent stages may add both new layers as well as new feature channels to the previous ones. Importantly, intermediate feature maps are shared among classifiers, preventing them from the necessity of being recomputed. To accomplish this, the model is trained end-to-end in a principled way under a joint loss. We validate our approach in theory and on a synthetic benchmark. As a result demonstrated in three applications (patch matching, object detection, and image retrieval), our cascade can operate significantly faster than both monolithic networks and traditional cascades without sharing at the cost of marginal decrease in precision.Comment: Accepted to BMVC 201

What makes for effective detection proposals?

Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines regarding proposal repeatability, ground truth annotation recall on PASCAL, ImageNet, and MS COCO, and their impact on DPM, R-CNN, and Fast R-CNN detection performance. Our analysis shows that for object detection improving proposal localisation accuracy is as important as improving recall. We introduce a novel metric, the average recall (AR), which rewards both high recall and good localisation and correlates surprisingly well with detection performance. Our findings show common strengths and weaknesses of existing methods, and provide insights and metrics for selecting and tuning proposal methods.Comment: TPAMI final version, duplicate proposals removed in experiment

Tensor Representations for Object Classification and Detection

A key problem in object recognition is finding a suitable object representation. For historical and computational reasons, vector descriptions that encode particular statistical properties of the data have been broadly applied. However, employing tensor representation can describe the interactions of multiple factors inherent to image formation. One of the most convenient uses for tensors is to represent complex objects in order to build a discriminative description. Thus thesis has several main contributions, focusing on visual data detection (e.g. of heads or pedestrians) and classification (e.g. of head or human body orientation) in still images and on machine learning techniques to analyse tensor data. These applications are among the most studied in computer vision and are typically formulated as binary or multi-class classification problems. The applicative context of this thesis is the video surveillance, where classification and detection tasks can be very hard, due to the scarce resolution and the noise characterising sensor data. Therefore, the main goal in that context is to design algorithms that can characterise different objects of interest, especially when immersed in a cluttered background and captured at low resolution. In the different amount of machine learning approaches, the ensemble-of-classifiers demonstrated to reach excellent classification accuracy, good generalisation ability, and robustness of noisy data. For these reasons, some approaches in that class have been adopted as basic machine classification frameworks to build robust classifiers and detectors. Moreover, also kernel machines has been exploited for classification purposes, since they represent a natural learning framework for tensors