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Multi-view 3D reconstruction generally adopts the feature fusion strategy to guide the generation of 3D shape for objects with different views. Empirically, the correspondence learning of object regions across different views enables better feature fusion. However, such idea has not been fully exploited in existing methods. Furthermore, current methods fail to explore the intrinsic dependency among regions within a 3D shape, leading to a rough reconstruction result. To address the above issues, we propose a Dual-View 3D Point Cloud reconstruction architecture named DVPC, which takes two views images as inputs, and progressively generates a refined 3D point cloud. First, a point cloud generation network is assigned to generate a coarse point cloud for each input view. Second, a dual-view point clouds synthesis network is presented in DVPC. It constructs a regional attention mechanism to learn a high-quality correspondence among regions across two coarse point clouds in different views, so that our DVPC can achieve feature fusion accurately. And then it develops a point cloud deformation module to produce a relatively-precise point cloud via establishing the communication between the coarse point cloud and the fused feature. Lastly, a point-region transformer network is devised to model the dependency among regions within the relatively-precise point cloud. With the dependency, the relatively-precise point cloud is refined into a desirable 3D point cloud with rich details. Qualitative and quantitative experiments on the ShapeNet and Pix3D datasets demonstrate that the proposed DVPC outperforms the state-of-the-art methods in terms of reconstruction quality. [ABSTRACT FROM AUTHOR]

Video captioning is a significant challenging task in computer vision and natural language processing, aiming to automatically describe video content by natural language sentences. Comprehensive understanding of video is the key for accurate video captioning, which needs to not only capture the global content and salient objects in video, but also understand the spatio-temporal relations of objects, including their temporal trajectories and spatial relationships. Thus, it is important for video captioning to capture the objects’ relationships both within and across frames. Therefore, in this paper, we propose an object-aware spatio-temporal graph (OSTG) approach for video captioning. It constructs spatio-temporal graphs to depict objects with their relations, where the temporal graphs represent objects’ inter-frame dynamics, and the spatial graphs represent objects’ intra-frame interactive relationships. The main novelties and advantages are: (1) Bidirectional temporal alignment: Bidirectional temporal graph is constructed along and reversely along the temporal order to perform bidirectional temporal alignment for objects across different frames, which provides complementary clues to capture the inter-frame temporal trajectories for each salient object. (2) Graph based spatial relation learning: Spatial relation graph is constructed among objects in each frame by considering their relative spatial locations and semantic correlations, which is exploited to learn relation features that encode intra-frame relationships for salient objects. (3) Object-aware feature aggregation: Trainable VLAD (vector of locally aggregated descriptors) models are deployed to perform object-aware feature aggregation on objects’ local features, which learn discriminative aggregated representations for better video captioning. A hierarchical attention mechanism is also developed to distinguish contributions of different object instances. Experiments on two widely-used datasets, MSR-VTT and MSVD, demonstrate our proposed approach achieves state-of-the-art performances in terms of BLEU@4, METEOR and CIDEr metrics. [ABSTRACT FROM AUTHOR]

The rapidly developing information technology leads to a fast growth of visual and textual contents, and it comes with huge challenges to make correlation and perform cross-media retrieval between images and sentences. Existing methods mainly explore cross-media correlation from either global-level instances as the whole images and sentences, or local-level fine-grained patches as the discriminative image regions and key words, which ignore the complementary information from the relation between local-level fine-grained patches. Naturally, relation understanding is highly important for learning cross-media correlation. People focus on not only the alignment between discriminative image regions and key words, but also their relations lying in the visual and textual context. Therefore, in this paper, we propose Multi-level Adaptive Visual-textual Alignment (MAVA) approach with the following contributions. First, we propose cross-media multi-pathway fine-grained network to extract not only the local fine-grained patches as discriminative image regions and key words, but also visual relations between image regions as well as textual relations from the context of sentences, which contain complementary information to exploit fine-grained characteristics within different media types. Second, we propose visual-textual bi-attention mechanism to distinguish the fine-grained information with different saliency from both local and relation levels, which can provide more discriminative hints for correlation learning. Third, we propose cross-media multi-level adaptive alignment to explore global, local and relation alignments. An adaptive alignment strategy is further proposed to enhance the matched pairs of different media types, and discard those misalignments adaptively to learn more precise cross-media correlation. Extensive experiments are conducted to perform image-sentence matching on 2 widely-used cross-media datasets, namely Flickr-30K and MS-COCO, comparing with 10 state-of-the-art methods, which can fully verify the effectiveness of our proposed MAVA approach. [ABSTRACT FROM AUTHOR]

This paper presents contextual kernel and spectral methods for learning the semantics of images that allow us to automatically annotate an image with keywords. First, to exploit the context of visual words within images for automatic image annotation, we define a novel spatial string kernel to quantify the similarity between images. Specifically, we represent each image as a 2-D sequence of visual words and measure the similarity between two 2-D sequences using the shared occurrences of s-length 1-D subsequences by decomposing each 2-D sequence into two orthogonal 1-D sequences. Based on our proposed spatial string kernel, we further formulate automatic image annotation as a contextual keyword propagation problem, which can be solved very efficiently by linear programming. Unlike the traditional relevance models that treat each keyword independently, the proposed contextual kernel method for keyword propagation takes into account the semantic context of annotation keywords and propagates multiple keywords simultaneously. Significantly, this type of semantic context can also be incorporated into spectral embedding for refining the annotations of images predicted by keyword propagation. Experiments on three standard image datasets demonstrate that our contextual kernel and spectral methods can achieve significantly better results than the state of the art. [ABSTRACT FROM AUTHOR]