# DenseAttentionSeg: Segment Hands from Interacted Objects Using Depth Input

## TL;DR

DenseAttentionSeg is a real-time deep neural network that segments hands and objects from depth data using a dense attention mechanism, contour loss, and a new large-scale dataset, outperforming existing methods.

## Contribution

The paper introduces DenseAttentionSeg with a novel dense attention mechanism, contour loss, and the InterSegHands dataset for improved interaction segmentation.

## Key findings

- Outperforms state-of-the-art segmentation methods
- Achieves real-time processing speeds
- Provides a large-scale dataset for hand-object interactions

## Abstract

We propose a real-time DNN-based technique to segment hand and object of interacting motions from depth inputs. Our model is called DenseAttentionSeg, which contains a dense attention mechanism to fuse information in different scales and improves the results quality with skip-connections. Besides, we introduce a contour loss in model training, which helps to generate accurate hand and object boundaries. Finally, we propose and release our InterSegHands dataset, a fine-scale hand segmentation dataset containing about 52k depth maps of hand-object interactions. Our experiments evaluate the effectiveness of our techniques and datasets, and indicate that our method outperforms the current state-of-the-art deep segmentation methods on interaction segmentation.