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.

Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understand the global content of video, but also capture the detailed object information. Meanwhile, video representations have great impact on the quality of generated captions. Thus, it is important for video captioning to capture salient objects with their detailed temporal dynamics, and represent them using discriminative spatio-temporal representations. In this paper, we propose a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object-aware local feature aggregation on detected object regions. The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object. (2) Object-aware aggregation: Learnable VLAD (Vector of Locally Aggregated Descriptors) models are constructed on object temporal trajectories and global frame sequence, which performs object-aware aggregation to learn discriminative representations. A hierarchical attention mechanism is also developed to distinguish different contributions of multiple objects. Experiments on two widely-used datasets demonstrate our OA-BTG achieves state-of-the-art performance in terms of BLEU@4, METEOR and CIDEr metrics.

Video classification is highly important and has widespread applications, such as video search and intelligent surveillance. Video naturally contains both static and motion information, which can be represented by frames and optical flow, respectively. Recently, researchers have generally adopted deep networks to capture the static and motion information separately, which has two main limitations. First, the coexistence relationship between spatial and temporal attention is ignored, although they should be jointly modeled as the spatial and temporal evolutions of video to learn discriminative video features. Second, the strong complementarity between static and motion information is ignored, although they should be collaboratively learned to enhance each other. To address the above two limitations, this paper proposes the two-stream collaborative learning with spatial-temporal attention (TCLSTA) approach, which consists of two models. First, for the spatial-temporal attention model, the spatial-level attention emphasizes the salient regions in a frame, and the temporal-level attention exploits the discriminative frames in a video. They are mutually enhanced to jointly learn the discriminative static and motion features for better classification performance. Second, for the static-motion collaborative model, it not only achieves mutual guidance between static and motion information to enhance the feature learning but also adaptively learns the fusion weights of static and motion streams, thus exploiting the strong complementarity between static and motion information to improve video classification. Experiments on four widely used data sets show that our TCLSTA approach achieves the best performance compared with more than 10 state-of-the-art methods.