The Visual Extent of an Object

The visual extent of an object reaches beyond the object itself. This is a long standing fact in psychology and is reflected in image retrieval techniques which aggregate statistics from the whole image in order to identify the object within. However, it is unclear to what degree and how the visual extent of an object affects classification performance. In this paper we investigate the visual extent of an object on the Pascal VOC dataset using a Bag-of-Words implementation with (colour) SIFT descriptors. Our analysis is performed from two angles. (a) Not knowing the object location, we determine where in the image the support for object classification resides. We call this the normal situation. (b) Assuming that the object location is known, we evaluate the relative potential of the object and its surround, and of the object border and object interior. We call this the ideal situation. Our most important discoveries are: (i) Surroundings can adequately distinguish between groups of classes: furniture, animals, and land-vehicles. For distinguishing categories within one group the surroundings become a source of confusion. (ii) The physically rigid plane, bike, bus, car, and train classes are recognised by interior boundaries and shape, not by texture. The non-rigid animals dog, cat, cow, and sheep are recognised primarily by texture, i.e. fur, as their projected shape varies greatly. (iii) We confirm an early observation from human psychology (Biederman in Perceptual Organization, pp. 213-263, 1981): in the ideal situation with known object locations, recognition is no longer improved by considering surroundings. In contrast, in the normal situation with unknown object locations, the surroundings significantly contribute to the recognition of most classes

End-to-End Localization and Ranking for Relative Attributes

We propose an end-to-end deep convolutional network to simultaneously localize and rank relative visual attributes, given only weakly-supervised pairwise image comparisons. Unlike previous methods, our network jointly learns the attribute's features, localization, and ranker. The localization module of our network discovers the most informative image region for the attribute, which is then used by the ranking module to learn a ranking model of the attribute. Our end-to-end framework also significantly speeds up processing and is much faster than previous methods. We show state-of-the-art ranking results on various relative attribute datasets, and our qualitative localization results clearly demonstrate our network's ability to learn meaningful image patches.Comment: Appears in European Conference on Computer Vision (ECCV), 201

Receptive Field Block Net for Accurate and Fast Object Detection

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs. Conversely, some lightweight model based detectors fulfil real time processing, while their accuracies are often criticized. In this paper, we explore an alternative to build a fast and accurate detector by strengthening lightweight features using a hand-crafted mechanism. Inspired by the structure of Receptive Fields (RFs) in human visual systems, we propose a novel RF Block (RFB) module, which takes the relationship between the size and eccentricity of RFs into account, to enhance the feature discriminability and robustness. We further assemble RFB to the top of SSD, constructing the RFB Net detector. To evaluate its effectiveness, experiments are conducted on two major benchmarks and the results show that RFB Net is able to reach the performance of advanced very deep detectors while keeping the real-time speed. Code is available at https://github.com/ruinmessi/RFBNet.Comment: Accepted by ECCV 201

Realtime Video Classification Using Dense HOF/HOG

ABSTRACT The current state-of-the-art in Video Classification is based on Bag-of-Words using local visual descriptors. Most commonly these are Histogram of Oriented Gradient (HOG) and Histogram of Optical Flow (HOF) descriptors. While such system is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG and HOF descriptors and release Matlab code. (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method. (3) We investigate the trade-off between accuracy and computational efficiency for the video representation, using either a k-means or hierarchical k-means based visual vocabulary, a Random Forest based vocabulary or the Fisher kernel

A Diagram Is Worth A Dozen Images

Diagrams are common tools for representing complex concepts, relationships and events, often when it would be difficult to portray the same information with natural images. Understanding natural images has been extensively studied in computer vision, while diagram understanding has received little attention. In this paper, we study the problem of diagram interpretation and reasoning, the challenging task of identifying the structure of a diagram and the semantics of its constituents and their relationships. We introduce Diagram Parse Graphs (DPG) as our representation to model the structure of diagrams. We define syntactic parsing of diagrams as learning to infer DPGs for diagrams and study semantic interpretation and reasoning of diagrams in the context of diagram question answering. We devise an LSTM-based method for syntactic parsing of diagrams and introduce a DPG-based attention model for diagram question answering. We compile a new dataset of diagrams with exhaustive annotations of constituents and relationships for over 5,000 diagrams and 15,000 questions and answers. Our results show the significance of our models for syntactic parsing and question answering in diagrams using DPGs

Spatio-Temporal Object Detection Proposals

International audienceSpatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods

Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017)This paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors

Multi-cue Mid-level Grouping

Abstract. Region proposal methods provide richer object hypotheses than sliding windows with dramatically fewer proposals, yet they still number in the thousands. This large quantity of proposals typically re-sults from a diversification step that propagates bottom-up ambiguity in the form of proposals to the next processing stage. In this paper, we take a complementary approach in which mid-level knowledge is used to re-solve bottom-up ambiguity at an earlier stage to allow a further reduction in the number of proposals. We present a method for generating regions using the mid-level grouping cues of closure and symmetry. In doing so, we combine mid-level cues that are typically used only in isolation, and leverage them to produce fewer but higher quality proposals. We empha-size that our model is mid-level by learning it on a limited number of objects while applying it to different objects, thus demonstrating that it is transferable to other objects. In our quantitative evaluation, we 1) establish the usefulness of each grouping cue by demonstrating incre-mental improvement, and 2) demonstrate improvement on two leading region proposal methods with a limited budget of proposals.