# Video Fill In the Blank using LR/RL LSTMs with Spatial-Temporal Attentions

## TL;DR

This paper introduces a novel framework combining LR/RL LSTMs with spatial-temporal attention to accurately fill in missing words in video descriptions, handling complex sentence structures and multiple blanks.

## Contribution

It proposes a new encoding method using separate LSTMs and external memory for better sentence structure understanding, along with spatial-temporal attention for visual cues.

## Key findings

- Outperforms existing methods on VFIB tasks
- Effective in handling multiple blanks in sentences
- Demonstrates strong generalization to complex scenarios

## Abstract

Given a video and a description sentence with one missing word (we call it the "source sentence"), Video-Fill-In-the-Blank (VFIB) problem is to find the missing word automatically. The contextual information of the sentence, as well as visual cues from the video, are important to infer the missing word accurately. Since the source sentence is broken into two fragments: the sentence's left fragment (before the blank) and the sentence's right fragment (after the blank), traditional Recurrent Neural Networks cannot encode this structure accurately because of many possible variations of the missing word in terms of the location and type of the word in the source sentence. For example, a missing word can be the first word or be in the middle of the sentence and it can be a verb or an adjective. In this paper, we propose a framework to tackle the textual encoding: Two separate LSTMs (the LR and RL LSTMs) are employed to encode the left and right sentence fragments and a novel structure is introduced to combine each fragment with an "external memory" corresponding the opposite fragments. For the visual encoding, end-to-end spatial and temporal attention models are employed to select discriminative visual representations to find the missing word. In the experiments, we demonstrate the superior performance of the proposed method on challenging VFIB problem. Furthermore, we introduce an extended and more generalized version of VFIB, which is not limited to a single blank. Our experiments indicate the generalization capability of our method in dealing with such more realistic scenarios.