Videobert Cloze Task

VideoBERT: Cloze Task Pre-training

VideoBERT is a multimodal transformer model pre-trained on instructional videos. This approach leverages the inherent alignment between spoken language and visual content in these videos, allowing VideoBERT to learn effective joint representations.

Why Instructional Videos?

Instructional videos, such as cooking tutorials, provide a strong temporal and semantic correlation between what is spoken and what is shown. For example, in a cooking video, a speaker might say "Cut the lemon into slices" while simultaneously demonstrating the action of slicing a lemon on screen. This direct alignment enables VideoBERT to more efficiently learn how language and visual information relate to each other.

Pre-training Process: Token Extraction

The pre-training process involves extracting two types of tokens from the instructional videos: linguistic and visual.

Linguistic Tokens

1. Audio Extraction: The audio track is first extracted from the video.

2. Speech-to-Text Conversion: Automatic Speech Recognition (ASR) tools are employed to convert the extracted audio into text.

3. Text Tokenization: The transcribed text is then tokenized, breaking it down into individual language tokens.

Visual Tokens

1. Frame Sampling: Image frames are sampled from the video at a rate of 20 frames per second (fps).

2. Clip Grouping: These sampled frames are grouped into short clips, each lasting 1.5 seconds.

3. Visual Token Generation: Each clip is then converted into a single visual token. This process essentially creates a discrete representation for a short segment of visual action or appearance.

Preparing the Input Sequence

The extracted linguistic and visual tokens are then combined into a single input sequence for the model, using special tokens to delineate different parts of the sequence. This structured sequence allows the transformer to process both modalities simultaneously.

The input sequence is constructed as follows:

• [CLS] Token: Added at the beginning of the entire sequence. This token is typically used for classification tasks and helps aggregate information from the entire sequence.

• Linguistic Tokens: The sequence of tokenized words from the spoken language.

• [>] Token: Inserted between the last linguistic token and the first visual token. This special token explicitly marks the boundary between the language and visual modalities.

• Visual Tokens: The sequence of visual tokens representing segments of the video.

• [SEP] Token: Added at the end of the entire sequence. This token signifies the end of the input sequence.

The resulting structure treats the combined language and visual sequence as a single, unified input.

Example Input Sequence Structure:

[CLS] Linguistic_Token_1 Linguistic_Token_2 ... Linguistic_Token_N [>] Visual_Token_1 Visual_Token_2 ... Visual_Token_M [SEP]

Applying the Cloze Task

The core of VideoBERT's pre-training is a cloze task, where some of the tokens in the prepared input sequence (both linguistic and visual) are randomly masked. The entire sequence, with masked tokens, is then fed into the VideoBERT model.

The model processes this masked sequence and generates contextual representations for each token. For instance:

• R[CLS]: The contextual representation of the [CLS] token.

• RCut: The contextual representation of the word "cut".

• R[slice-frame]: The contextual representation of a visual token that depicts a slicing action.

These contextual representations are then passed through a classifier (a feedforward layer followed by a softmax function) to predict the original, masked tokens. By performing this task across both modalities, VideoBERT learns to understand and relate language and visual information.

SEO Keywords

• VideoBERT instructional video pre-training

• Linguistic and visual token extraction VideoBERT

• Automatic Speech Recognition in VideoBERT

• Visual token generation from video frames

• VideoBERT input sequence preparation

• Special tokens in VideoBERT input ([CLS], [SEP], [>])

• Cloze task on multimodal tokens VideoBERT

• Joint language and video representation learning

Interview Questions

• Why does VideoBERT prioritize instructional videos over random videos for pre-training?

• Can you detail the steps involved in extracting linguistic tokens for VideoBERT?

• What is the specific role of Automatic Speech Recognition (ASR) in the VideoBERT pre-training pipeline?

• Describe the methodology for generating visual tokens from video frames in VideoBERT.

• Explain how the combined input sequence for VideoBERT is prepared and structured.

• What is the purpose and function of the special tokens like [CLS], [SEP], and [>] within the VideoBERT input?

• How is the cloze task implemented in VideoBERT, specifically concerning both language and visual tokens?

• What do the contextual token representations, such as R[CLS] or RCut, signify within VideoBERT's framework?

• How does VideoBERT reconstruct the original tokens after they have been masked?

• Elaborate on how VideoBERT effectively learns joint representations from multimodal inputs.