Egocentric Video-Language Pretraining (2206.01670v2)

Abstract: Video-Language Pretraining (VLP), which aims to learn transferable representation to advance a wide range of video-text downstream tasks, has recently received increasing attention. Best performing works rely on large-scale, 3rd-person video-text datasets, such as HowTo100M. In this work, we exploit the recently released Ego4D dataset to pioneer Egocentric VLP along three directions. (i) We create EgoClip, a 1st-person video-text pretraining dataset comprising 3.8M clip-text pairs well-chosen from Ego4D, covering a large variety of human daily activities. (ii) We propose a novel pretraining objective, dubbed EgoNCE, which adapts video-text contrastive learning to the egocentric domain by mining egocentric-aware positive and negative samples. (iii) We introduce EgoMCQ, a development benchmark that is close to EgoClip and hence can support effective validation and fast exploration of our design decisions in EgoClip and EgoNCE. Furthermore, we demonstrate strong performance on five egocentric downstream tasks across three datasets: video-text retrieval on EPIC-KITCHENS-100; action recognition on Charades-Ego; natural language query, moment query, and object state change classification on Ego4D challenge benchmarks. The dataset and code are available at https://github.com/showlab/EgoVLP.

An Analysis of Egocentric Video-Language Pretraining

The paper "Egocentric Video-Language Pretraining" offers a comprehensive investigation into the domain of Egocentric Video-Language Pretraining (VLP). The authors put forward a novel approach specific to first-person perspective videos, which has been somewhat neglected compared to their more traditional third-person counterparts. The research utilizes the newly introduced large-scale Ego4D dataset to overcome existing limitations and create a foundation for Egocentric VLP.

The primary contributions of the work can be summarized as follows:

1. EgoClip Dataset: The paper introduces EgoClip, a novel video-text pretraining dataset composed of 3.8 million clip-text pairs curated from the Ego4D dataset. EgoClip represents a diverse range of human daily activities and is designed to fill the gap between existing third-person datasets and the specific needs of egocentric video applications.
2. Pretraining Objective - EgoNCE: A novel pretraining objective, EgoNCE, is proposed. This objective is tailored to the egocentric domain by incorporating action-aware positive and scene-aware negative sampling strategies to enhance contrastive learning. EgoNCE aims to address unique challenges such as repetitive actions across different scenarios and similar actions within the same scene.
3. Development Benchmark - EgoMCQ: The authors propose EgoMCQ, a benchmark developed from the EgoClip dataset. This benchmark is designed to provide an effective platform for evaluating and refining VLP models specifically for the egocentric domain, catering to both inter-video and intra-video challenges.

The paper's experimental section demonstrates the effectiveness of Egocentric VLP across multiple tasks, including video-text retrieval, action recognition, and various language query challenges. The research shows superior performance compared to state-of-the-art methods using third-person datasets, signifying the importance of domain-specific pretraining.

EgoClip, the dataset, is significant because it addresses the scarcity of large-scale, egocentric video-text datasets. By covering a wide range of everyday human activities, it allows for the development of models that are more contextually aware of the nuances present in first-person videos. Such models are crucial for advancing applications in robotics, augmented reality, and other fields that rely on immersive video understanding.

The introduction of the EgoNCE objective is another pivotal aspect of this paper. By focusing on action-aware and scene-aware sampling, EgoNCE effectively captures the unique characteristics of egocentric videos, where the same action might appear in multiple contexts and different actions might be visually similar. This fine-grained learning is important for generating robust and transfer-ready video-text representations.

EgoMCQ further complements the pretraining endeavors by providing a benchmark that's closely aligned with the characteristics of the training data. This setup allows for reliable model validation and swift exploration of design decisions, ensuring that innovations in the dataset and objective functions are effectively translated into performance gains on downstream tasks.

In terms of future directions, this paper lays a foundational step towards recognizing the need for egocentric-specific video-text datasets and pretraining strategies. As the field progresses, there are opportunities to further explore long-term dependencies within these datasets, integrate cross-modal semantics in richer ways, and examine the transferability of these pretrained models to more complex, real-world tasks. Additionally, attention to privacy and bias considerations inherent in egocentric videos will be necessary as the reliance on personal and intimate viewpoints increases.

Overall, the research encapsulates a focused attempt to bridge the gap between egocentric and third-person video language pretraining, providing a methodological framework and a comprehensive set of tools to encourage further exploration in this space.