Regularized Deep Network Learning For Multi-Label Visual Recognition

This dissertation is focused on the task of multi-label visual recognition, a fundamental task of computer vision. It aims to tell the presence of multiple visual classes from the input image, where the visual classes, such as objects, scenes, attributes, etc., are usually defined as image labels. Due to the prosperous deep networks, this task has been widely studied and significantly improved in recent years. However, it remains a challenging task due to appearance complexity of multiple visual contents co-occurring in one image. This research explores to regularize the deep network learning for multi-label visual recognition. First, an attention concentration method is proposed to refine the deep network learning for human attribute recognition, i.e., a challenging instance of multi-label visual recognition. Here the visual attention of deep networks, in terms of attention maps, is an imitation of human attention in visual recognition. Derived by the deep network with only label-level supervision, attention maps interpretively highlight areas indicating the most relevant regions that contribute most to the final network prediction. Based on the observation that human attributes are usually depicted by local image regions, the added attention concentration enhances the deep network learning for human attribute recognition by forcing the recognition on compact attribute-relevant regions. Second, inspired by the consistent relevance between a visual class and an image region, an attention consistency strategy is explored and enforced during deep network learning for human attribute recognition. Specifically, two kinds of attention consistency are studied in this dissertation, including the equivariance under spatial transforms, such as flipping, scaling and rotation, and the invariance between different networks for recognizing the same attribute from the same image. These two kinds of attention consistency are formulated as a unified attention consistency loss and combined with the traditional classification loss for network learning. Experiments on public datasets verify its effectiveness by achieving new state-of-the-art performance for human attribute recognition. Finally, to address the long-tailed category distribution of multi-label visual recognition, the collaborative learning between using uniform and re-balanced samplings is proposed for regularizing the network training. While the uniform sampling leads to relatively low performance on tail classes, re-balanced sampling can improve the performance on tail classes, but may also hurt the performance on head classes in network training due to label co-occurrence. This research proposes a new approach to train on both class-biased samplings in a collaborative way, resulting in performance improvement for both head and tail classes. Based on a two-branch network taking the uniform sampling and re-balanced sampling as the inputs, respectively, a cross-branch loss enforces consistency when the same input goes through the two branches. The experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods on long-tailed multi-label visual recognition

Collaborative Learning of Fine-grained Visual Data

Problem: Deep learning based vision systems have achieved near human accuracy in recognizing coarse object categories from visual data. But recognizing fine-grained sub-categories remains an open problem. Tasks like fine-grained species recognition poses further challenges: significant background variation compared to subtle difference between objects, high class imbalance due to scarcity of samples for endangered species, cost of domain expert annotations and labeling, etc. Methodology: The existing approaches, like transfer learning, to solve the problem of learning small specialized datasets are still inadequate in case of fine-grained sub-categories. The hypothesis of this work is that collaborative filters should be incorporated into the present learning frameworks to better address these challenges. The intuition comes from the fact that collaborative representation based classifiers have been earlier used for face recognition problems which present similar challenges. Outcomes: Keeping the above hypothesis in mind, the thesis achieves the following objectives: 1) It demonstrates the suitability of collaborative classifiers for fine-grained recognition 2) It expands the state-of-the-art by incorporating automated background suppression into collaborative classification formulation 3) It incorporates the collaborative cost function into supervised learning (deep convolutional network) and unsupervised learning (clustering algorithms) 4) Lastly, during the work several benchmark fine-grained image datasets have been introduced on NZ and Indian butterflies and bird species recognition

Robust Mobile Visual Recognition System: From Bag of Visual Words to Deep Learning

With billions of images captured by mobile users everyday, automatically recognizing contents in such images has become a particularly important feature for various mobile apps, including augmented reality, product search, visual-based authentication etc. Traditionally, a client-server architecture is adopted such that the mobile client sends captured images/video frames to a cloud server, which runs a set of task-specific computer vision algorithms and sends back the recognition results. However, such scheme may cause problems related to user privacy, network stability/availability and device energy.In this dissertation, we investigate the problem of building a robust mobile visual recognition system that achieves high accuracy, low latency, low energy cost and privacy protection. Generally, we study two broad types of recognition methods: the bag of visual words (BOVW) based retrieval methods, which search the nearest neighbor image to a query image, and the state-of-the-art deep learning based methods, which recognize a given image using a trained deep neural network. The challenges of deploying BOVW based retrieval methods include: size of indexed image database, query latency, feature extraction efficiency and re-ranking performance. To address such challenges, we first proposed EMOD which enables efficient on-device image retrieval on a downloaded context-dependent partial image database. The efficiency is achieved by analyzing the BOVW processing pipeline and optimizing each module with algorithmic improvement.Recent deep learning based recognition approaches have been shown to greatly exceed the performance of traditional approaches. We identify several challenges of applying deep learning based recognition methods on mobile scenarios, namely energy efficiency and privacy protection for real-time visual processing, and mobile visual domain biases. Thus, we proposed two techniques to address them, (i) efficiently splitting the workload across heterogeneous computing resources, i.e., mobile devices and the cloud using our Moca framework, and (ii) using mobile visual domain adaptation as proposed in our collaborative edge-mediated platform DeepCham. Our extensive experiments on large-scale benchmark datasets and off-the-shelf mobile devices show our solutions provide better results than the state-of-the-art solutions

Collaborative Feature Learning from Social Media

Image feature representation plays an essential role in image recognition and related tasks. The current state-of-the-art feature learning paradigm is supervised learning from labeled data. However, this paradigm requires large-scale category labels, which limits its applicability to domains where labels are hard to obtain. In this paper, we propose a new data-driven feature learning paradigm which does not rely on category labels. Instead, we learn from user behavior data collected on social media. Concretely, we use the image relationship discovered in the latent space from the user behavior data to guide the image feature learning. We collect a large-scale image and user behavior dataset from Behance.net. The dataset consists of 1.9 million images and over 300 million view records from 1.9 million users. We validate our feature learning paradigm on this dataset and find that the learned feature significantly outperforms the state-of-the-art image features in learning better image similarities. We also show that the learned feature performs competitively on various recognition benchmarks

Collaborative Layer-wise Discriminative Learning in Deep Neural Networks

Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before camera-ready versio

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502