[Paper Review] Video Captioning and Retrieval Models with Semantic Attention.

Youngjae Yu, Hyungjin Ko+2 more|arXiv (Cornell University)|Oct 10, 2016

Multimodal Machine Learning ApplicationsComputer Science3 references37 citations

TL;DR

This paper proposes a semantic attention mechanism integrated with a concept word detector that enhances video captioning and retrieval models by generating semantic priors directly from video input without external knowledge. The end-to-end trainable detector identifies relevant concept words, which are then selectively attended to in the language generation process, achieving state-of-the-art performance on three of four LSMDC 2016 tasks, including fill-in-the-blank and movie retrieval.

ABSTRACT

We propose a high-level concept word detector that can be integrated with any video-to-language models. It takes a video as input and generates a list of concept words as useful semantic priors for language generation models. The proposed word detector has two important properties. First, it does not require any external knowledge sources for training. Second, the proposed word detector is trainable in an end-to-end manner jointly with any video-to-language models. To maximize the values of detected words, we also develop a semantic attention mechanism that selectively focuses on the detected concept words and fuse them with the word encoding and decoding in the language model. In order to demonstrate that the proposed approach indeed improves the performance of multiple video-to-language tasks, we participate in four tasks of LSMDC 2016. Our approach achieves the best accuracies in three of them, including fill-in-the-blank, multiple-choice test, and movie retrieval. We also attain comparable performance for the other task, movie description.

Motivation & Objective

• To improve video-to-language models by incorporating semantic priors derived directly from video input.
• To develop a concept word detector that requires no external knowledge sources for training.
• To enable end-to-end joint training of the concept detector with video-to-language models.
• To enhance language generation by selectively focusing on detected concept words using a semantic attention mechanism.
• To demonstrate performance gains across multiple video-to-language tasks, including captioning and retrieval.

Proposed method

• A high-level concept word detector is trained end-to-end on video input to output a list of relevant semantic concepts without relying on external knowledge.
• The detected concept words serve as semantic priors to guide language generation in video captioning and retrieval models.
• A semantic attention mechanism is introduced to selectively attend to the detected concept words during both encoding and decoding in the language model.
• The entire system, including the concept detector and attention mechanism, is trained jointly with the video-to-language model in an end-to-end fashion.
• The method is evaluated on four tasks from the LSMDC 2016 benchmark, including fill-in-the-blank, multiple-choice, movie retrieval, and description generation.

Experimental results

Research questions

• RQ1 Can a concept word detector trained end-to-end on video input improve video captioning and retrieval performance without external knowledge?
• RQ2 How effective is a semantic attention mechanism in focusing on detected concept words during language generation?
• RQ3 Does integrating detected semantic priors lead to consistent performance gains across diverse video-to-language tasks?
• RQ4 Can the proposed method achieve state-of-the-art results on multiple video understanding benchmarks?

Key findings

• The proposed approach achieved the best accuracy on the fill-in-the-blank task in the LSMDC 2016 challenge.
• It achieved the highest performance on the multiple-choice test task among all submitted methods.
• The model obtained the best results on the movie retrieval task, demonstrating strong semantic alignment between video and text.
• For the movie description task, the method achieved comparable performance to the state of the art, indicating strong generalization across different captioning styles.