Binary Representation Learning for Large Scale Visual Data

The exponentially growing modern media created large amount of multimodal or multidomain visual data, which usually reside in high dimensional space. And it is crucial to provide not only effective but also efficient understanding of the data.In this dissertation, we focus on learning binary representation of visual dataset, whose primary use has been hash code for retrieval purpose. Simultaneously it serves as multifunctional feature that can also be used for various computer vision tasks. Essentially, this is achieved by discriminative learning that preserves the supervision information in the binary representation.By using deep networks such as convolutional neural networks (CNNs) as backbones, and effective binary embedding algorithm that is seamlessly integrated into the learning process, we achieve state-of-the art performance on several settings. First, we study the supervised binary representation learning problem by using label information directly instead of pairwise similarity or triplet loss. By considering images and associated textual information, we study the cross-modal representation learning. CNNs are used in both image and text embedding, and we are able to perform retrieval and prediction across these modalities. Furthermore, by utilizing unlabeled images from a different domain, we propose to use adversarial learning to connect these domains. Finally, we also consider progressive learning for more efficient learning and instance-level representation learning to provide finer granularity understanding. This dissertation demonstrates that binary representation is versatile and powerful under various circumstances with different tasks

Multimodal sequential fashion attribute prediction

We address multimodal product attribute prediction of fashion items based on product images and titles. The product attributes, such as type, sub-type, cut or fit, are in a chain format, with previous attribute values constraining the values of the next attributes. We propose to address this task with a sequential prediction model that can learn to capture the dependencies between the different attribute values in the chain. Our experiments on three product datasets show that the sequential model outperforms two non-sequential baselines on all experimental datasets. Compared to other models, the sequential model is also better able to generate sequences of attribute chains not seen during training. We also measure the contributions of both image and textual input and show that while text-only models always outperform image-only models, only the multimodal sequential model combining both image and text improves over the text-only model on all experimental dataset

The Emerging Trends of Multi-Label Learning

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.Comment: Accepted to TPAMI 202

PatchCT: Aligning Patch Set and Label Set with Conditional Transport for Multi-Label Image Classification

Multi-label image classification is a prediction task that aims to identify more than one label from a given image. This paper considers the semantic consistency of the latent space between the visual patch and linguistic label domains and introduces the conditional transport (CT) theory to bridge the acknowledged gap. While recent cross-modal attention-based studies have attempted to align such two representations and achieved impressive performance, they required carefully-designed alignment modules and extra complex operations in the attention computation. We find that by formulating the multi-label classification as a CT problem, we can exploit the interactions between the image and label efficiently by minimizing the bidirectional CT cost. Specifically, after feeding the images and textual labels into the modality-specific encoders, we view each image as a mixture of patch embeddings and a mixture of label embeddings, which capture the local region features and the class prototypes, respectively. CT is then employed to learn and align those two semantic sets by defining the forward and backward navigators. Importantly, the defined navigators in CT distance model the similarities between patches and labels, which provides an interpretable tool to visualize the learned prototypes. Extensive experiments on three public image benchmarks show that the proposed model consistently outperforms the previous methods. Our code is available at https://github.com/keepgoingjkg/PatchCT.Comment: accepted by ICCV2