MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer

TL;DR

MED-VT++ introduces a unified multiscale transformer architecture for dense video prediction, leveraging multimodal inputs and a transductive learning scheme to improve accuracy and temporal consistency without optical flow.

Contribution

The paper proposes a novel end-to-end multiscale encoder-decoder transformer for video segmentation, incorporating multimodal processing and a transductive learning scheme for enhanced performance.

Findings

Outperforms state-of-the-art on multiple benchmarks

Effective without optical flow reliance

Demonstrates strong multimodal segmentation results

Abstract

In this paper, we present an end-to-end trainable unified multiscale encoder-decoder transformer that is focused on dense prediction tasks in video. The presented Multiscale Encoder-Decoder Video Transformer (MED-VT) uses multiscale representation throughout and employs an optional input beyond video (e.g., audio), when available, for multimodal processing (MED-VT++). Multiscale representation at both encoder and decoder yields three key benefits: (i) implicit extraction of spatiotemporal features at different levels of abstraction for capturing dynamics without reliance on input optical flow, (ii) temporal consistency at encoding and (iii) coarse-to-fine detection for high-level (e.g., object) semantics to guide precise localization at decoding. Moreover, we present a transductive learning scheme through many-to-many label propagation to provide temporally consistent video predictions. We showcase MED-VT/MED-VT++ on three unimodal video segmentation tasks (Automatic Video Object Segmentation (AVOS), actor-action segmentation and Video Semantic Segmentation (VSS)) as well as a multimodal segmentation task (Audio-Visual Segmentation (AVS)). Results show that the proposed architecture outperforms alternative state-of-the-art approaches on multiple benchmarks using only video (and optional audio) as input, without reliance on optical flow. Finally, to document details of the model's internal learned representations, we present a detailed interpretability study, encompassing both quantitative and qualitative analyses.