Multi-modal Deep Analysis for Multimedia

With the rapid development of Internet and multimedia services in the past decade, a huge amount of user-generated and service provider-generated multimedia data become available. These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them. Multi-modal data consist of a mixture of various types of data from different modalities such as texts, images, videos, audios etc. In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia. We introduce two scientific research problems, data-driven correlational representation and knowledge-guided fusion for multimedia analysis. To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2) multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge. More specifically, on data-driven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal hashing. On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation. Finally, we bring forward our insights and future research directions.

Double-Head RCNN: Rethinking Classification and Localization for Object Detection

Modern R-CNN based detectors apply a head to extract Region of Interest (RoI) features for both classification and localization tasks. In contrast, we found that these two tasks have opposite preferences towards two widely used head structures (i.e. fully connected head and convolution head). Specifically, the fully connected head is more suitable for the classification task, while the convolution head is more suitable for the localization task. Therefore, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head to pay more attention to bounding box regression. In addition, we have two findings for the unfocused tasks (i.e. classification in the convolution head, and bounding box regression in the fully connected head): (a) adding classification to the convolution head is complementary to the classification in the fully connected head, and (b) bounding box regression provides auxiliary supervision for the fully connected head. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.

Rethinking Classification and Localization in R-CNN

Modern R-CNN based detectors share the RoI feature extractor head for both classification and localization tasks, based upon the correlation between the two tasks. In contrast, we found that different head structures (i.e. fully connected head and convolution head) have opposite preferences towards these two tasks. Specifically, the fully connected head is more suitable for the classification task, while the convolution head is more suitable for the localization task. Therefore, we propose a double-head method to separate these two tasks into different heads (i.e. a fully connected head for classification and a convolution head for box regression). Without bells and whistles, our method gains +3.4 and +2.7 points mAP on MS COCO dataset from Feature Pyramid Network baselines with ResNet-50 and ResNet-101 backbones, respectively.

PatternNet: Visual Pattern Mining with Deep Neural Network

Visual patterns represent the discernible regularity in the visual world. They capture the essential nature of visual objects or scenes. Understanding and modeling visual patterns is a fundamental problem in visual recognition that has wide ranging applications. In this paper, we study the problem of visual pattern mining and propose a novel deep neural network architecture called PatternNet for discovering these patterns that are both discriminative and representative. The proposed PatternNet leverages the filters in the last convolution layer of a convolutional neural network to find locally consistent visual patches, and by combining these filters we can effectively discover unique visual patterns. In addition, PatternNet can discover visual patterns efficiently without performing expensive image patch sampling, and this advantage provides an order of magnitude speedup compared to most other approaches. We evaluate the proposed PatternNet subjectively by showing randomly selected visual patterns which are discovered by our method and quantitatively by performing image classification with the identified visual patterns and comparing our performance with the current state-of-the-art. We also directly evaluate the quality of the discovered visual patterns by leveraging the identified patterns as proposed objects in an image and compare with other relevant methods. Our proposed network and procedure, PatterNet, is able to outperform competing methods for the tasks described.

Event Specific Multimodal Pattern Mining with Image-Caption Pairs

In this paper we describe a novel framework and algorithms for discovering image patch patterns from a large corpus of weakly supervised image-caption pairs generated from news events. Current pattern mining techniques attempt to find patterns that are representative and discriminative, we stipulate that our discovered patterns must also be recognizable by humans and preferably with meaningful names. We propose a new multimodal pattern mining approach that leverages the descriptive captions often accompanying news images to learn semantically meaningful image patch patterns. The mutltimodal patterns are then named using words mined from the associated image captions for each pattern. A novel evaluation framework is provided that demonstrates our patterns are 26.2% more semantically meaningful than those discovered by the state of the art vision only pipeline, and that we can provide tags for the discovered images patches with 54.5% accuracy with no direct supervision. Our methods also discover named patterns beyond those covered by the existing image datasets like ImageNet. To the best of our knowledge this is the first algorithm developed to automatically mine image patch patterns that have strong semantic meaning specific to high-level news events, and then evaluate these patterns based on that criteria.