Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network (1908.10072v1)

Abstract: In this paper, we propose to guide the video caption generation with Part-of-Speech (POS) information, based on a gated fusion of multiple representations of input videos. We construct a novel gated fusion network, with one particularly designed cross-gating (CG) block, to effectively encode and fuse different types of representations, e.g., the motion and content features of an input video. One POS sequence generator relies on this fused representation to predict the global syntactic structure, which is thereafter leveraged to guide the video captioning generation and control the syntax of the generated sentence. Specifically, a gating strategy is proposed to dynamically and adaptively incorporate the global syntactic POS information into the decoder for generating each word. Experimental results on two benchmark datasets, namely MSR-VTT and MSVD, demonstrate that the proposed model can well exploit complementary information from multiple representations, resulting in improved performances. Moreover, the generated global POS information can well capture the global syntactic structure of the sentence, and thus be exploited to control the syntactic structure of the description. Such POS information not only boosts the video captioning performance but also improves the diversity of the generated captions. Our code is at: https://github.com/vsislab/Controllable_XGating.

An Examination of Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network

The paper "Controllable Video Captioning with POS Sequence Guidance Based on Gated Fusion Network" proposes a model that enhances the generation of video captions by integrating Part-of-Speech (POS) information through a specialized gated fusion network. This approach underscores the importance of utilizing syntactic guidance in generating video descriptions, aiming for effective cross-modal representation and syntactic control over the generated text.

The essence of this work lies in the introduction of a gated fusion framework that effectively combines various video representations. This framework features a unique cross-gating (CG) mechanism designed to mutually influence diverse semantic features, such as content and motion information derived from video inputs. The cross-gating block selectively enhances pertinent feature elements, thereby fostering a comprehensive understanding of the video data. This nuanced fusion of features surpasses conventional concatenation methods by considering inter-feature dependencies, thus providing a robust basis for caption generation.

Integral to this model is the POS sequence generator. Utilizing the encoded video representations, this component predicts the global syntactic structure of the sentence to be generated. This predicted POS sequence acts as a strategic guide during the captioning process, offering a controlled syntactic trajectory. By embedding the global POS information into the decoder, the model dynamically and adaptively integrates syntactic cues at each step of word generation. Such a mechanism not only refines the semantic accuracy of captions but also introduces diversity in syntactic structures, an aspect often limited in existing paradigms.

Empirically, this model's efficacy was validated against two benchmark datasets, MSR-VTT and MSVD, where it demonstrated improved performance across multiple metrics, including BLEU, METEOR, ROUGE-L, and CIDEr. Particularly, reinforcement learning techniques further enhanced its performance, solidifying the role of POS guidance in achieving state-of-the-art results. Notably, the integration of syntactic information led to superior scores in ROUGE-L and CIDEr on both datasets.

The methodological novelty of this research extends beyond performance metrics; it provides a degree of control over the syntactic makeup of captions, which is a significant development in the field of automatic video description. By adjusting the predicted POS sequence, the model allows researchers to explore variations in sentence structure, potentially catering to diverse applications and linguistics preferences.

For future research, this proposition opens several avenues. An immediate direction would involve extending the gated fusion framework to incorporate additional modalities, such as audio or topic-specific cues, which could further enhance the semantic richness of captions. Additionally, exploring deeper syntactic and semantic interdependencies using advanced neural architectures or attention mechanisms may yield even finer control over the generated text. Moreover, the interpretability of the generated captions vis-à-vis POS control could be analyzed to further appreciate syntactic diversity.

In conclusion, this paper's contributions mark a substantial step toward more intelligent and adaptable video captioning systems. By leveraging POS information and a gated fusion network, the model not only advances the state-of-the-art in captioning accuracy but also enriches the field with innovative syntactic control capabilities, thereby opening pathways to more nuanced human-computer interactions.