COOT:Cooperative Hierarchical Transformer for Video-Text Representation Learning

COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning 을 읽고 정리한 글입니다.


Model

Abstract

COOT to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities.

The method consists of three major components: an attention-aware feature aggregation layer, which leverages the local temporal context (intra-level, e.g., within a clip), a contextual transformer to learn the interactions between low-level and high-level semantics (inter-level, e.g. clip-video, sentence-paragraph), and a cross-modal cycle-consistency loss to connect video and text.

Main Contribution

  1. Attention-aware feature aggregation
    • local temporal context
  2. Contextual Transformer
    • interactions bet. low-level & high-level semantics
  3. Cross-modal cycle-consistency loss
    • connect video and text

MODEL

  1. input : video, text (2-stream)
  2. encode frame-level/word-level features
  3. [2] are fed to temporal transformer (T-Transformer)
  4. [3] are fed to Attention feature aggregation module (Attention-FA) to obtain clip/sentence-level feature
  5. Contextual transformer produces the final video/paragraph embedding based on interactions bet. local context(clip/sentence features) & global context (all frames/words features) CoT

Introduction

we focus on long-range temporal dependencies and propose a hierarchical model that can exploit long-range temporal context both in videos and text when learning the joint cross-modal embedding.

  • 각 레벨 안에서 피쳐 추출하고 다음 레벨과 interaction하는 방식으로 계층을 쌓아 문제를 해결함.
  • hierarchy
    1. frame/word
    2. clip/sentence
    3. video/paragraph

Contribution #1. intra-level : Attention-FA

to model intra-level cooperation, we introduce an attention-aware feature aggregation layer to focus on temporal interactions between low-level entities (Figure 1-Attention-FA).

This component replaces traditional sequence representation aggregation methods in transformers such as using a [CLS] token [11, 14, 15, 19] or mean pooling [25] with an attention-aware fusion. It leverages temporal context to encourage important entities to contribute more to the final representation of a sequence of frames or words.

[CLS] 사용하는 것보다 낫다는 건 VideoBERT보다 좋다는 접근법이라는 걸 의미하는 건가

Contribution #2. inter-level : CoT

For the inter-level cooperation, we introduce a contextual attention module, which enforces the network to highlight semantics relevant to the general context of the video and to suppress the irrelevant semantics. This is done by modeling the interaction between low-level (clips-sentences) and high-level entities (global contexts), as shown in Figure 1-green region.

Contribution #3. Cross-modal cycle-consistency loss

to enforce interaction between modalities and encourage the semantic alignment between them in the learned common space.

CML

Experiments

Dataset

  • YouCook2
  • ActivityNet-captions

Evaluation

  • For video captioning : BLEU@3/4, RougeL, METEOR, CIDEr-D

Video Encoding

For Activitynet-Captions, we use the 2048-d features provided by Zhang et al. [21] (at 3.8 FPS).

For Youcook2, we test two approaches: (A) We follow Miech et al., 2019 [16] and concatenate 2D (Resnet-152 pretrained on ImageNet [41]) and 3D (ResNext-101 model [42] pretrained on Kinetics [43]) outputs to obtain 4096-d features at 3 FPS; (B) We use the video embedding network provided by Miech et al., 2020 [17] pretrained on video-text learning on the Howto100m dataset to obtain 512-d features at 0.6 FPS.

Video Captioning

we use the learned representations for video captioning building upon the captioning model MART [45]. The original method uses appearance (RGB) and optical flow features extracted from ResNet-200 [41] and BN-Inception [46], respectively.

Result

Limitations

YouCook2에는 굉장히 괜찮은 결과가 나오나, ActivityNet 데이터셋에 대해서는 아직 만족할 수준은 아닌 것 같다.

  • Pretrain 데이터셋이 H100M, YouCook2이므로 ActivityNet 데이터셋과는 데이터의 특성이 다를 것이라 생각됨.
  • MART 위에 올린 것이므로 이 모델의 한계가 이어지지 않을까 싶음.