Timeline and Boundary Guided Diffusion Network for Video Shadow Detection

TL;DR

This paper introduces a novel diffusion-based network for video shadow detection that effectively integrates temporal guidance and boundary information, significantly improving detection accuracy over existing methods.

Contribution

It is the first to apply a diffusion model to VSD, incorporating a dual scale aggregation and boundary-aware attention to enhance temporal and boundary feature learning.

Findings

Outperforms state-of-the-art methods in VSD accuracy

Effectively captures temporal and boundary features

Demonstrates the effectiveness of diffusion models in VSD

Abstract

Video Shadow Detection (VSD) aims to detect the shadow masks with frame sequence. Existing works suffer from inefficient temporal learning. Moreover, few works address the VSD problem by considering the characteristic (i.e., boundary) of shadow. Motivated by this, we propose a Timeline and Boundary Guided Diffusion (TBGDiff) network for VSD where we take account of the past-future temporal guidance and boundary information jointly. In detail, we design a Dual Scale Aggregation (DSA) module for better temporal understanding by rethinking the affinity of the long-term and short-term frames for the clipped video. Next, we introduce Shadow Boundary Aware Attention (SBAA) to utilize the edge contexts for capturing the characteristics of shadows. Moreover, we are the first to introduce the Diffusion model for VSD in which we explore a Space-Time Encoded Embedding (STEE) to inject the temporal guidance for Diffusion to conduct shadow detection. Benefiting from these designs, our model can not only capture the temporal information but also the shadow property. Extensive experiments show that the performance of our approach overtakes the state-of-the-art methods, verifying the effectiveness of our components. We release the codes, weights, and results at \url{https://github.com/haipengzhou856/TBGDiff}.