Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation

TL;DR

This paper introduces a novel conditional GAN architecture that leverages multi-modal data and scene context to improve continuous fine-grained human action segmentation, outperforming existing methods on multiple datasets.

Contribution

The paper presents a dual-GAN framework with context extraction that effectively integrates multiple data modalities for enhanced action segmentation.

Findings

Outperforms state-of-the-art on 50 Salads, MERL Shopping, Georgia Tech datasets

Utilizes multi-modal data including RGB, depth, and optical flow

Demonstrates the importance of context and auxiliary information in segmentation

Abstract

We propose a novel conditional GAN (cGAN) model for continuous fine-grained human action segmentation, that utilises multi-modal data and learned scene context information. The proposed approach utilises two GANs: termed Action GAN and Auxiliary GAN, where the Action GAN is trained to operate over the current RGB frame while the Auxiliary GAN utilises supplementary information such as depth or optical flow. The goal of both GANs is to generate similar `action codes', a vector representation of the current action. To facilitate this process a context extractor that incorporates data and recent outputs from both modes is used to extract context information to aid recognition. The result is a recurrent GAN architecture which learns a task specific loss function from multiple feature modalities. Extensive evaluations on variants of the proposed model to show the importance of utilising different information streams such as context and auxiliary information in the proposed network; and show that our model is capable of outperforming state-of-the-art methods for three widely used datasets: 50 Salads, MERL Shopping and Georgia Tech Egocentric Activities, comprising both static and dynamic camera settings.