Visual Commonsense based Heterogeneous Graph Contrastive Learning

How to select relevant key objects and reason about the complex relationships cross vision and linguistic domain are two key issues in many multi-modality applications such as visual question answering (VQA). In this work, we incorporate the visual commonsense information and propose a heterogeneous graph contrastive learning method to better finish the visual reasoning task. Our method is designed as a plug-and-play way, so that it can be quickly and easily combined with a wide range of representative methods. Specifically, our model contains two key components: the Commonsense-based Contrastive Learning and the Graph Relation Network. Using contrastive learning, we guide the model concentrate more on discriminative objects and relevant visual commonsense attributes. Besides, thanks to the introduction of the Graph Relation Network, the model reasons about the correlations between homogeneous edges and the similarities between heterogeneous edges, which makes information transmission more effective. Extensive experiments on four benchmarks show that our method greatly improves seven representative VQA models, demonstrating its effectiveness and generalizability

Plug-and-Play Regulators for Image-Text Matching

Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity. However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback. In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Specifically, we propose (i) a Recurrent Correspondence Regulator (RCR) which facilitates the cross-modal attention unit progressively with adaptive attention factors to capture more flexible correspondence, and (ii) a Recurrent Aggregation Regulator (RAR) which adjusts the aggregation weights repeatedly to increasingly emphasize important alignments and dilute unimportant ones. Besides, it is interesting that RCR and RAR are plug-and-play: both of them can be incorporated into many frameworks based on cross-modal interaction to obtain significant benefits, and their cooperation achieves further improvements. Extensive experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models, confirming the general effectiveness and generalization ability of the proposed methods. Code and pre-trained models are available at: https://github.com/Paranioar/RCAR.Comment: 13 pages, 9 figures, Accepted by TIP202

An Attention-driven Hierarchical Multi-scale Representation for Visual Recognition

Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extract multi-scale localized features and compose them to construct highly expressive representations for decision making. However, the convolution operation is unable to capture long-range dependencies such as arbitrary relations between pixels since it operates on a fixed-size window. Therefore, it may not be suitable for discriminating subtle changes (e.g. fine-grained visual recognition). To this end, our proposed method captures the high-level long-range dependencies by exploring Graph Convolutional Networks (GCNs), which aggregate information by establishing relationships among multi-scale hierarchical regions. These regions consist of smaller (closer look) to larger (far look), and the dependency between regions is modeled by an innovative attention-driven message propagation, guided by the graph structure to emphasize the neighborhoods of a given region. Our approach is simple yet extremely effective in solving both the fine-grained and generic visual classification problems. It outperforms the state-of-the-arts with a significant margin on three and is very competitive on other two datasets.Comment: Accepted in the 32nd British Machine Vision Conference (BMVC) 202

Visual-Semantic Learning

Visual-semantic learning is an attractive and challenging research direction aiming to understand complex semantics of heterogeneous data from two domains, i.e., visual signals (i.e., images and videos) and natural language (i.e., captions and questions). It requires memorizing the rich information in a single modality and a joint comprehension of multiple modalities. Artificial intelligence (AI) systems with human-level intelligence are claimed to learn like humans, such as efficiently leveraging brain memory for better comprehension, rationally incorporating common-sense knowledge into reasoning, quickly gaining in-depth understanding given a few samples, and analyzing relationships among abundant and informative events. However, these intelligence capacities are effortless for humans but challenging for machines. To bridge the discrepancy between human-level intelligence and present-day visual-semantic learning, we start from its basic understanding ability by studying the visual question answering (e.g., Image-QA and Video-QA) tasks from the perspectives of memory augmentation and common-sense knowledge incorporation. Furthermore, we stretch it to a more challenging situation with limited and partially unlabeled training data (i.e., Few-shot Visual-Semantic Learning) to imitate the fast learning ability of humans. Finally, to further enhance visual-semantic performance in natural videos with numerous spatio-temporal dynamics, we investigate exploiting event-correlated information for a comprehensive understanding of cross-modal semantics. To study the essential visual-semantic understanding ability of the human brain with memory, we first propose a novel Memory Augmented Deep Recurrent Neural Network (i.e., MA-DRNN) model for Video-QA, which features a new method for encoding videos and questions, and memory augmentation using the emerging Differentiable Neural Computer (i.e., DNC). Specifically, we encode semantic (i.e., questions) information before visual (i.e., videos) information, which leads to better visual-semantic representations. Moreover, we leverage Differentiable Neural Computer (with external memory) to store and retrieve valuable information in questions and videos and model the long-term visual-semantic dependency. In addition to basic understanding, to tackle visual-semantic reasoning that requires external knowledge beyond visible contents (e.g., KB-Image-QA), we propose a novel framework that endows the model with capabilities of answering more general questions and achieves better exploitation of external knowledge through generating Multiple Clues for Reasoning with Memory Neural Networks (i.e., MCR-MemNN). Specifically, a well-defined detector is adopted to predict image-question-related relation phrases, each delivering two complementary clues to retrieve the supporting facts from an external knowledge base (i.e., KB). These facts are encoded into a continuous embedding space using a content-addressable memory. Afterward, mutual interactions between visual-semantic representation and the supporting facts stored in memory are captured to distill the most relevant information in three modalities (i.e., image, question, and KB). Finally, the optimal answer is predicted by choosing the supporting fact with the highest score. Furthermore, to enable a fast, in-depth understanding given a small number of samples, especially with heterogeneity in the multi-modal scenarios such as image question answering (i.e., Image-QA) and image captioning (i.e., IC), we study the few-shot visual-semantic learning and present the Hierarchical Graph ATtention Network (i.e., HGAT). This two-stage network models the intra- and inter-modal relationships with limited image-text samples. The main contributions of HGAT can be summarized as follows: 1) it sheds light on tackling few-shot multi-modal learning problems, which focuses primarily, but not exclusively, on visual and semantic modalities, through better exploitation of the intra-relationship of each modality and an attention-based co-learning framework between modalities using a hierarchical graph-based architecture; 2) it achieves superior performance on both visual question answering and image captioning in the few-shot setting; 3) it can be easily extended to the semi-supervised setting where image-text samples are partially unlabeled. Although various attention mechanisms have been utilized to manage contextualized representations by modeling intra- and inter-modal relationships of the two modalities, one limitation of the predominant visual-semantic methods is the lack of reasoning with event correlation, sensing, and analyzing relationships among abundant and informative events contained in the video. To this end, we introduce the dense caption modality as a new auxiliary and distill event-correlated information to infer the correct answer. We propose a novel end-to-end trainable model, Event-Correlated Graph Neural Networks (EC-GNNs), to perform cross-modal reasoning over information from the three modalities (i.e., caption, video, and question). Besides exploiting a new modality, we employ cross-modal reasoning modules to explicitly model inter-modal relationships and aggregate relevant information across different modalities. We propose a question-guided self-adaptive multi-modal fusion module to collect the question-oriented and event-correlated evidence through multi-step reasoning. To evaluate our proposed models, we conduct extensive experiments on VTW, MSVD-QA, and TGIF-QA datasets for Video-QA task, Toronto COCO-QA, Visual Genome-QA datasets for few-shot Image-QA task, COCO-FITB dataset for few-shot IC task, and FVQA, Visual7W + ConceptNet datasets for KB-Image-QA task. The experimental results justify these models’ effectiveness and superiority over baseline methods

A Survey of Natural Language Generation

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-to-text generation deep learning methods, as well as new applications of NLG technology. This survey aims to (a) give the latest synthesis of deep learning research on the NLG core tasks, as well as the architectures adopted in the field; (b) detail meticulously and comprehensively various NLG tasks and datasets, and draw attention to the challenges in NLG evaluation, focusing on different evaluation methods and their relationships; (c) highlight some future emphasis and relatively recent research issues that arise due to the increasing synergy between NLG and other artificial intelligence areas, such as computer vision, text and computational creativity.Comment: Accepted by ACM Computing Survey (CSUR) 202

Improving Cross-Modal Retrieval with Set of Diverse Embeddings

Cross-modal retrieval across image and text modalities is a challenging task due to its inherent ambiguity: An image often exhibits various situations, and a caption can be coupled with diverse images. Set-based embedding has been studied as a solution to this problem. It seeks to encode a sample into a set of different embedding vectors that capture different semantics of the sample. In this paper, we present a novel set-based embedding method, which is distinct from previous work in two aspects. First, we present a new similarity function called smooth-Chamfer similarity, which is designed to alleviate the side effects of existing similarity functions for set-based embedding. Second, we propose a novel set prediction module to produce a set of embedding vectors that effectively captures diverse semantics of input by the slot attention mechanism. Our method is evaluated on the COCO and Flickr30K datasets across different visual backbones, where it outperforms existing methods including ones that demand substantially larger computation at inference.Comment: Accepted to CVPR 2023 (Highlight