Recurrent Memory Addressing for describing videos

TL;DR

This paper presents a novel recurrent key-value memory network for video captioning that decomposes visual and language processing, capturing spatial dependencies and improving performance on standard benchmarks.

Contribution

It introduces a recurrent key-addressing mechanism for key-value memory networks in video captioning, enabling better spatial and temporal modeling.

Findings

Achieves competitive BLEU@4 and METEOR scores on Youtube2Text

Demonstrates the effectiveness of recurrent key-addressing in video captioning

Captures spatial dependencies in visual-to-semantic mapping

Abstract

In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. We propose to find the next step attention weights conditioned on the previous attention distributions for the key-value memory slots in the memory addressing schema. Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Experiments done on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.