Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval

Image-sentence retrieval has attracted extensive research attention in multimedia and computer vision due to its promising application. The key issue lies in jointly learning the visual and textual representation to accurately estimate their similarity. To this end, the mainstream schema adopts an object-word based attention to calculate their relevance scores and refine their interactive representations with the attention features, which, however, neglects the context of the object representation on the inter-object relationship that matches the predicates in sentences. In this paper, we propose a Cross-modal Semantic Enhanced Interaction method, termed CMSEI for image-sentence retrieval, which correlates the intra- and inter-modal semantics between objects and words. In particular, we first design the intra-modal spatial and semantic graphs based reasoning to enhance the semantic representations of objects guided by the explicit relationships of the objects' spatial positions and their scene graph. Then the visual and textual semantic representations are refined jointly via the inter-modal interactive attention and the cross-modal alignment. To correlate the context of objects with the textual context, we further refine the visual semantic representation via the cross-level object-sentence and word-image based interactive attention. Experimental results on seven standard evaluation metrics show that the proposed CMSEI outperforms the state-of-the-art and the alternative approaches on MS-COCO and Flickr30K benchmarks.

Global2Local: A Joint-Hierarchical Attention for Video Captioning

Recently, automatic video captioning has attracted increasing attention, where the core challenge lies in capturing the key semantic items, like objects and actions as well as their spatial-temporal correlations from the redundant frames and semantic content. To this end, existing works select either the key video clips in a global level~(across multi frames), or key regions within each frame, which, however, neglect the hierarchical order, i.e., key frames first and key regions latter. In this paper, we propose a novel joint-hierarchical attention model for video captioning, which embeds the key clips, the key frames and the key regions jointly into the captioning model in a hierarchical manner. Such a joint-hierarchical attention model first conducts a global selection to identify key frames, followed by a Gumbel sampling operation to identify further key regions based on the key frames, achieving an accurate global-to-local feature representation to guide the captioning. Extensive quantitative evaluations on two public benchmark datasets MSVD and MSR-VTT demonstrates the superiority of the proposed method over the state-of-the-art methods.

Factored Attention and Embedding for Unstructured-view Topic-related Ultrasound Report Generation

Echocardiography is widely used to clinical practice for diagnosis and treatment, e.g., on the common congenital heart defects. The traditional manual manipulation is error-prone due to the staff shortage, excess workload, and less experience, leading to the urgent requirement of an automated computer-aided reporting system to lighten the workload of ultrasonologists considerably and assist them in decision making. Despite some recent successful attempts in automatical medical report generation, they are trapped in the ultrasound report generation, which involves unstructured-view images and topic-related descriptions. To this end, we investigate the task of the unstructured-view topic-related ultrasound report generation, and propose a novel factored attention and embedding model (termed FAE-Gen). The proposed FAE-Gen mainly consists of two modules, i.e., view-guided factored attention and topic-oriented factored embedding, which 1) capture the homogeneous and heterogeneous morphological characteristic across different views, and 2) generate the descriptions with different syntactic patterns and different emphatic contents for different topics. Experimental evaluations are conducted on a to-be-released large-scale clinical cardiovascular ultrasound dataset (CardUltData). Both quantitative comparisons and qualitative analysis demonstrate the effectiveness and the superiority of FAE-Gen over seven commonly-used metrics.

Differentiated Relevances Embedding for Group-based Referring Expression Comprehension

Referring expression comprehension (REC) aims to locate a certain object in an image referred by a natural language expression. For joint understanding of regions and expressions, existing REC works typically target on modeling the cross-modal relevance in each region-expression pair within each single image. In this paper, we explore a new but general REC-related problem, named Group-based REC, where the regions and expressions can come from different subject-related images (images in the same group), e.g., sets of photo albums or video frames. Different from REC, Group-based REC involves differentiated cross-modal relevances within each group and across different groups, which, however, are neglected in the existing one-line paradigm. To this end, we propose a novel relevance-guided multi-group self-paced learning schema (termed RMSL), where the within-group region-expression pairs are adaptively assigned with different priorities according to their cross-modal relevances, and the bias of the group priority is balanced via an across-group relevance constraint simultaneously. In particular, based on the visual and textual semantic features, RMSL conducts an adaptive learning cycle upon triplet ranking, where (1) the target-negative region-expression pairs with low within-group relevances are used preferentially in model training to distinguish the primary semantics of the target objects, and (2) an across-group relevance regularization is integrated into model training to balance the bias of group priority. The relevances, the pairs, and the model parameters are alternatively updated upon a unified self-paced hinge loss.

Weakly-Supervised Dense Action Anticipation

Dense anticipation aims to forecast future actions and their durations for long horizons. Existing approaches rely on fully-labelled data, i.e. sequences labelled with all future actions and their durations. We present a (semi-) weakly supervised method using only a small number of fully-labelled sequences and predominantly sequences in which only the (one) upcoming action is labelled. To this end, we propose a framework that generates pseudo-labels for future actions and their durations and adaptively refines them through a refinement module. Given only the upcoming action label as input, these pseudo-labels guide action/duration prediction for the future. We further design an attention mechanism to predict context-aware durations. Experiments on the Breakfast and 50Salads benchmarks verify our method's effectiveness; we are competitive even when compared to fully supervised state-of-the-art models. We will make our code available at: https://github.com/zhanghaotong1/WSLVideoDenseAnticipation.

Structured Multi-modal Feature Embedding and Alignment for Image-Sentence Retrieval

The current state-of-the-art image-sentence retrieval methods implicitly align the visual-textual fragments, like regions in images and words in sentences, and adopt attention modules to highlight the relevance of cross-modal semantic correspondences. However, the retrieval performance remains unsatisfactory due to a lack of consistent representation in both semantics and structural spaces. In this work, we propose to address the above issue from two aspects: (i) constructing intrinsic structure (along with relations) among the fragments of respective modalities, e.g., "dog $\to$ play $\to$ ball" in semantic structure for an image, and (ii) seeking explicit inter-modal structural and semantic correspondence between the visual and textual modalities. In this paper, we propose a novel Structured Multi-modal Feature Embedding and Alignment (SMFEA) model for image-sentence retrieval. In order to jointly and explicitly learn the visual-textual embedding and the cross-modal alignment, SMFEA creates a novel multi-modal structured module with a shared context-aware referral tree. In particular, the relations of the visual and textual fragments are modeled by constructing Visual Context-aware Structured Tree encoder (VCS-Tree) and Textual Context-aware Structured Tree encoder (TCS-Tree) with shared labels, from which visual and textual features can be jointly learned and optimized. We utilize the multi-modal tree structure to explicitly align the heterogeneous image-sentence data by maximizing the semantic and structural similarity between corresponding inter-modal tree nodes. Extensive experiments on Microsoft COCO and Flickr30K benchmarks demonstrate the superiority of the proposed model in comparison to the state-of-the-art methods.

Improving Image Captioning by Leveraging Intra- and Inter-layer Global Representation in Transformer Network

Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. In GET, a Global Enhanced Encoder is designed for the embedding of the global feature, and a Global Adaptive Decoder are designed for the guidance of the caption generation. The former models intra- and inter-layer global representation by taking advantage of the proposed Global Enhanced Attention and a layer-wise fusion module. The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation. Extensive experiments on MS COCO dataset demonstrate the superiority of our GET over many state-of-the-arts.

Semantic-aware Image Deblurring

Image deblurring has achieved exciting progress in recent years. However, traditional methods fail to deblur severely blurred images, where semantic contents appears ambiguously. In this paper, we conduct image deblurring guided by the semantic contents inferred from image captioning. Specially, we propose a novel Structured-Spatial Semantic Embedding model for image deblurring (termed S3E-Deblur), which introduces a novel Structured-Spatial Semantic tree model (S3-tree) to bridge two basic tasks in computer vision: image deblurring (ImD) and image captioning (ImC). In particular, S3-tree captures and represents the semantic contents in structured spatial features in ImC, and then embeds the spatial features of the tree nodes into GAN based ImD. Co-training on S3-tree, ImC, and ImD is conducted to optimize the overall model in a multi-task end-to-end manner. Extensive experiments on severely blurred MSCOCO and GoPro datasets demonstrate the significant superiority of S3E-Deblur compared to the state-of-the-arts on both ImD and ImC tasks.

Scene-based Factored Attention for Image Captioning

Image captioning has attracted ever-increasing research attention in the multimedia community. To this end, most cutting-edge works rely on an encoder-decoder framework with attention mechanisms, which have achieved remarkable progress. However, such a framework does not consider scene concepts to attend visual information, which leads to sentence bias in caption generation and defects the performance correspondingly. We argue that such scene concepts capture higher-level visual semantics and serve as an important cue in describing images. In this paper, we propose a novel scene-based factored attention module for image captioning. Specifically, the proposed module first embeds the scene concepts into factored weights explicitly and attends the visual information extracted from the input image. Then, an adaptive LSTM is used to generate captions for specific scene types. Experimental results on Microsoft COCO benchmark show that the proposed scene-based attention module improves model performance a lot, which outperforms the state-of-the-art approaches under various evaluation metrics.