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

Fusion of Multimodal Information in Music Content Analysis

Music is often processed through its acoustic realization. This is restrictive in the sense that music is clearly a highly multimodal concept where various types of heterogeneous information can be associated to a given piece of music (a musical score, musicians\u27 gestures, lyrics, user-generated metadata, etc.). This has recently led researchers to apprehend music through its various facets, giving rise to "multimodal music analysis" studies. This article gives a synthetic overview of methods that have been successfully employed in multimodal signal analysis. In particular, their use in music content processing is discussed in more details through five case studies that highlight different multimodal integration techniques. The case studies include an example of cross-modal correlation for music video analysis, an audiovisual drum transcription system, a description of the concept of informed source separation, a discussion of multimodal dance-scene analysis, and an example of user-interactive music analysis. In the light of these case studies, some perspectives of multimodality in music processing are finally suggested

Expanding Language-Image Pretrained Models for General Video Recognition

Contrastive language-image pretraining has shown great success in learning visual-textual joint representation from web-scale data, demonstrating remarkable "zero-shot" generalization ability for various image tasks. However, how to effectively expand such new language-image pretraining methods to video domains is still an open problem. In this work, we present a simple yet effective approach that adapts the pretrained language-image models to video recognition directly, instead of pretraining a new model from scratch. More concretely, to capture the long-range dependencies of frames along the temporal dimension, we propose a cross-frame attention mechanism that explicitly exchanges information across frames. Such module is lightweight and can be plugged into pretrained language-image models seamlessly. Moreover, we propose a video-specific prompting scheme, which leverages video content information for generating discriminative textual prompts. Extensive experiments demonstrate that our approach is effective and can be generalized to different video recognition scenarios. In particular, under fully-supervised settings, our approach achieves a top-1 accuracy of 87.1% on Kinectics-400, while using 12 times fewer FLOPs compared with Swin-L and ViViT-H. In zero-shot experiments, our approach surpasses the current state-of-the-art methods by +7.6% and +14.9% in terms of top-1 accuracy under two popular protocols. In few-shot scenarios, our approach outperforms previous best methods by +32.1% and +23.1% when the labeled data is extremely limited. Code and models are available at https://aka.ms/X-CLIPComment: Accepted by ECCV2022, Ora

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

VLANet: Video-Language Alignment Network for Weakly-Supervised Video Moment Retrieval

Video Moment Retrieval (VMR) is a task to localize the temporal moment in untrimmed video specified by natural language query. For VMR, several methods that require full supervision for training have been proposed. Unfortunately, acquiring a large number of training videos with labeled temporal boundaries for each query is a labor-intensive process. This paper explores methods for performing VMR in a weakly-supervised manner (wVMR): training is performed without temporal moment labels but only with the text query that describes a segment of the video. Existing methods on wVMR generate multi-scale proposals and apply query-guided attention mechanisms to highlight the most relevant proposal. To leverage the weak supervision, contrastive learning is used which predicts higher scores for the correct video-query pairs than for the incorrect pairs. It has been observed that a large number of candidate proposals, coarse query representation, and one-way attention mechanism lead to blurry attention maps which limit the localization performance. To handle this issue, Video-Language Alignment Network (VLANet) is proposed that learns sharper attention by pruning out spurious candidate proposals and applying a multi-directional attention mechanism with fine-grained query representation. The Surrogate Proposal Selection module selects a proposal based on the proximity to the query in the joint embedding space, and thus substantially reduces candidate proposals which leads to lower computation load and sharper attention. Next, the Cascaded Cross-modal Attention module considers dense feature interactions and multi-directional attention flow to learn the multi-modal alignment. VLANet is trained end-to-end using contrastive loss which enforces semantically similar videos and queries to gather. The experiments show that the method achieves state-of-the-art performance on Charades-STA and DiDeMo datasets.Comment: 16 pages, 6 figures, European Conference on Computer Vision, 202