A latent variable ranking model for content-based retrieval

34th European Conference on IR Research, ECIR 2012, Barcelona, Spain, April 1-5, 2012. ProceedingsSince their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of users of the retrieval system. Most previous ranking models learn either a global combination of elementary similarity functions or a combination defined with respect to a single database item. Instead, we propose a “coarse to fine” ranking model where given a query we first compute a distribution over “coarse” classes and then use the linear combination that has been optimized for queries of that class. These coarse classes are hidden and need to be induced by the training algorithm. We propose a latent variable ranking model that induces both the latent classes and the weights of the linear combination for each class from ranking triplets. Our experiments over two large image datasets and a text retrieval dataset show the advantages of our model over learning a global combination as well as a combination for each test point (i.e. transductive setting). Furthermore, compared to the transductive approach our model has a clear computational advantages since it does not need to be retrained for each test query.Spanish Ministry of Science and Innovation (JCI-2009-04240)EU PASCAL2 Network of Excellence (FP7-ICT-216886

Transfer Learning using Computational Intelligence: A Survey

Abstract Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. In contrast to classical machine learning methods, transfer learning methods exploit the knowledge accumulated from data in auxiliary domains to facilitate predictive modeling consisting of different data patterns in the current domain. To improve the performance of existing transfer learning methods and handle the knowledge transfer process in real-world systems, ..

DEEP NEURAL NETWORKS AND REGRESSION MODELS FOR OBJECT DETECTION AND POSE ESTIMATION

Estimating the pose, orientation and the location of objects has been a central problem addressed by the computer vision community for decades. In this dissertation, we propose new approaches for these important problems using deep neural networks as well as tree-based regression models. For the first topic, we look at the human body pose estimation problem and propose a novel regression-based approach. The goal of human body pose estimation is to predict the locations of body joints, given an image of a person. Due to significant variations introduced by pose, clothing and body styles, it is extremely difficult to address this task by a standard application of the regression method. Thus, we address this task by dividing the whole body pose estimation problem into a set of local pose estimation problems by introducing a dependency graph which describes the dependency among different body joints. For each local pose estimation problem, we train a boosted regression tree model and estimate the pose by progressively applying the regression along the paths in a dependency graph starting from the root node. Our next work is on improving the traditional regression tree method and demonstrate its effectiveness for pose/orientation estimation tasks. The main issues of the traditional regression training are, 1) the node splitting is limited to binary splitting, 2) the form of the splitting function is limited to thresholding on a single dimension of the input vector and 3) the best splitting function is found by exhaustive search. We propose a novel node splitting algorithm for regression tree training which does not have the issues mentioned above. The algorithm proceeds by first applying k-means clustering in the output space, conducting multi-class classification by support vector machine (SVM) and determining the constant estimate at each leaf node. We apply the regression forest that includes our regression tree models to head pose estimation, car orientation estimation and pedestrian orientation estimation tasks and demonstrate its superiority over various standard regression methods. Next, we turn our attention to the role of pose information for the object detection task. In particular, we focus on the detection of fashion items a person is wearing or carrying. It is clear that the locations of these items are strongly correlated with the pose of the person. To address this task, we first generate a set of candidate bounding boxes by using an object proposal algorithm. For each candidate bounding box, image features are extracted by a deep convolutional neural network pre-trained on a large image dataset and the detection scores are generated by SVMs. We introduce a pose-dependent prior on the geometry of the bounding boxes and combine it with the SVM scores. We demonstrate that the proposed algorithm achieves significant improvement in the detection performance. Lastly, we address the object detection task by exploring a way to incorporate an attention mechanism into the detection algorithm. Humans have the capability of allocating multiple fixation points, each of which attends to different locations and scales of the scene. However, such a mechanism is missing in the current state-of-the-art object detection methods. Inspired by the human vision system, we propose a novel deep network architecture that imitates this attention mechanism. For detecting objects in an image, the network adaptively places a sequence of glimpses at different locations in the image. Evidences of the presence of an object and its location are extracted from these glimpses, which are then fused for estimating the object class and bounding box coordinates. Due to the lack of ground truth annotations for the visual attention mechanism, we train our network using a reinforcement learning algorithm. Experiment results on standard object detection benchmarks show that the proposed network consistently outperforms the baseline networks that do not employ the attention mechanism

Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective

This paper takes a problem-oriented perspective and presents a comprehensive review of transfer learning methods, both shallow and deep, for cross-dataset visual recognition. Specifically, it categorises the cross-dataset recognition into seventeen problems based on a set of carefully chosen data and label attributes. Such a problem-oriented taxonomy has allowed us to examine how different transfer learning approaches tackle each problem and how well each problem has been researched to date. The comprehensive problem-oriented review of the advances in transfer learning with respect to the problem has not only revealed the challenges in transfer learning for visual recognition, but also the problems (e.g. eight of the seventeen problems) that have been scarcely studied. This survey not only presents an up-to-date technical review for researchers, but also a systematic approach and a reference for a machine learning practitioner to categorise a real problem and to look up for a possible solution accordingly