VIOLET : End-to-End Video-Language Transformers with Masked Visual-token Modeling (2111.12681v2)

Abstract: A great challenge in video-language (VidL) modeling lies in the disconnection between fixed video representations extracted from image/video understanding models and downstream VidL data. Recent studies try to mitigate this disconnection via end-to-end training. To make it computationally feasible, prior works tend to "imagify" video inputs, i.e., a handful of sparsely sampled frames are fed into a 2D CNN, followed by a simple mean-pooling or concatenation to obtain the overall video representations. Although achieving promising results, such simple approaches may lose temporal information that is essential for performing downstream VidL tasks. In this work, we present VIOLET, a fully end-to-end VIdeO-LanguagE Transformer, which adopts a video transformer to explicitly model the temporal dynamics of video inputs. Further, unlike previous studies that found pre-training tasks on video inputs (e.g., masked frame modeling) not very effective, we design a new pre-training task, Masked Visual-token Modeling (MVM), for better video modeling. Specifically, the original video frame patches are "tokenized" into discrete visual tokens, and the goal is to recover the original visual tokens based on the masked patches. Comprehensive analysis demonstrates the effectiveness of both explicit temporal modeling via video transformer and MVM. As a result, VIOLET achieves new state-of-the-art performance on 5 video question answering tasks and 4 text-to-video retrieval tasks.

Overview of VIOLET: End-to-End Video-Language Transformers

The paper "VIOLET: End-to-End Video-Language Transformers with Masked Visual-token Modeling" addresses key advancements in video-language (VidL) modeling, primarily focusing on two components: direct temporal modeling with Video Swin Transformer and novel pre-training tasks, particularly Masked Visual-token Modeling (MVM). This paper proposes VIOLET, a comprehensive end-to-end framework that builds upon recent efforts to bridge the disconnection between fixed video representations and downstream VidL tasks through more nuanced modeling of video temporal dynamics and context.

The presented architecture diverges from earlier methods that oversimplify video representations by merely imagifying video inputs, which involves processing sparse frames through 2D convolutional neural networks (CNNs). Such methods often ignore the temporal dimension's richness, pivotal in capturing video content pertinent to VidL tasks. VIOLET's approach employs the Video Swin Transformer, which integrates temporal dimension into video representations concurrently with spatial information. This provides a sophisticated representation learning process that maintains essential video dynamics.

A standout contribution of the paper is the introduction of the MVM pre-training task, a method that enhances video understanding by "tokenizing" video frame patches. These tokens serve as discrete learning objectives, where the challenge is to reconstruct the original tokens from masked video inputs. This approach contrasts with less effective previous attempts like Masked Frame Modeling (MFM), by focusing on discrete token spaces that mitigate training complexities tied to continuous, high-dimensional feature spaces.

The empirical results demonstrate that VIOLET sets new standards on several VidL tasks, including video question answering and text-to-video retrieval benchmarks. Specifically, it achieves state-of-the-art performance across five video question answering tasks and four text-to-video retrieval tasks, showcasing its broad applicability and efficacy.

From a practical perspective, this research offers a refined architectural blueprint that can be readily adapted to existing methodologies for improved video-language integration. Theoretical implications are substantial, proposing a shift in how temporal modeling and token-based pre-training tasks are conceptualized in multilayered video content understanding.

Looking forward, the developments presented in VIOLET lay a foundation for future explorations in AI, particularly in constructing more holistic video and language understanding systems. This involves expanding the pre-training datasets and refining temporal encoding methods to further extend the model's applicability across varied domiciles exceeding the current benchmarks. Future work might opine on integrating additional modalities, such as audio semantics, to create a more versatile AI capable of nuanced and context-dependent reasoning across multimedia content.

In conclusion, VIOLET presents a robust advancement by addressing key deficiencies in prior VidL models, offering a solid framework with promising scalability and applicability in AI domains reliant on video-language tasks.