LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at https://github.com/Bartzi/loan

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

Geometry Constrained Weakly Supervised Object Localization

We propose a geometry constrained network, termed GC-Net, for weakly supervised object localization (WSOL). GC-Net consists of three modules: a detector, a generator and a classifier. The detector predicts the object location defined by a set of coefficients describing a geometric shape (i.e. ellipse or rectangle), which is geometrically constrained by the mask produced by the generator. The classifier takes the resulting masked images as input and performs two complementary classification tasks for the object and background. To make the mask more compact and more complete, we propose a novel multi-task loss function that takes into account area of the geometric shape, the categorical cross-entropy and the negative entropy. In contrast to previous approaches, GC-Net is trained end-to-end and predict object location without any post-processing (e.g. thresholding) that may require additional tuning. Extensive experiments on the CUB-200-2011 and ILSVRC2012 datasets show that GC-Net outperforms state-of-the-art methods by a large margin. Our source code is available at https://github.com/lwzeng/GC-Net.Comment: This paper (ID 5424) is accepted to ECCV 202

Image Co-localization by Mimicking a Good Detector's Confidence Score Distribution

Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods.Comment: Accepted to Proc. European Conf. Computer Vision 201

FIRE in ImageCLEF 2005: Combining Content-Based Image Retrieval with Textual Information Retrieval

Abstract. In this paper the methods we used in the 2005 ImageCLEF content-based image retrieval evaluation are described. For the medical retrieval task, we combined several low-level image features with textual information retrieval. Combining these two information sources, clear improvements over the use of one of these sources alone are possible. Additionally we participated in the automatic annotation task, where our content-based image retrieval system, FIRE, was used as well as a second subimage based method for object classification. The results we achieved are very convincing. Our submissions ranked first and the third in the automatic annotation task out of a total of 44 submissions from 12 groups.

A Benchmark for Content-Based Retrieval in Bivariate Data Collections

Huge amounts of various research data are produced and made publicly available in digital libraries. An important category is bivariate data (measurements of one variable versus the other). Examples of bivariate data include observations of temperature and ozone levels (e.g., in environmental observation), domestic production and unemployment (e.g., in economics), or education and income level levels (in the social sciences). For accessing these data, content-based retrieval is an important query modality. It allows researchers to search for specific relationships among data variables (e.g., quadratic dependence of temperature on altitude). However, such retrieval is to date a challenge, as it is not clear which similarity measures to apply. Various approaches have been proposed, yet no benchmarks to compare their retrieval effectiveness have been defined. In this paper, we construct a benchmark for retrieval of bivariate data. It is based on a large collection of bivariate research data. To define similarity classes, we use category information that was annotated by domain experts. The resulting similarity classes are used to compare several recently proposed content-based retrieval approaches for bivariate data, by means of precision and recall. This study is the first to present an encompassing benchmark data set and compare the performance of respective techniques. We also identify potential research directions based on the results obtained for bivariate data. The benchmark and implementations of similarity functions are made available, to foster research in this emerging area of content-based retrieval