# ATDMNet: Multi-Head Agent Attention and Top-k Dynamic Mask for Camouflaged Object Detection

## TL;DR

This paper introduces ATDMNet, a new method for detecting camouflaged objects using a combination of CNNs and transformers with attention mechanisms.

## Contribution

The novel ATDMNet architecture integrates multi-head agent attention and top-k dynamic mask for improved camouflaged object detection.

## Key findings

- ATDMNet outperforms existing methods in COD datasets like NC4K and COD10K.
- The use of MHA and TDM improves feature sensitivity and segmentation accuracy.

## Abstract

Camouflaged object detection (COD) encounters substantial difficulties owing to the visual resemblance between targets and their environments, together with discrepancies in multiscale representation of features. Current methodologies confront obstacles with feature distraction, modeling far-reaching dependencies, fusing multiple-scale details, and extracting boundary specifics. Consequently, we propose ATDMNet, an amalgamated architecture combining CNN and transformer within a numerous phases feature extraction framework. ATDMNet employs Res2Net as the foundational encoder and incorporates two essential components: multi-head agent attention (MHA) and top-k dynamic mask (TDM). MHA improves local feature sensitivity and long-range dependency modeling by incorporating agent nodes and positional biases, whereas TDM boosts attention with top-k operations and multiscale dynamic methods. The decoding phase utilizes bilinear upsampling and sophisticated semantic guidance to enhance low-level characteristics, hence ensuring precise segmentation. Enhanced performance is achieved by deep supervision and a hybrid loss function. Experiments applying COD datasets (NC4K, COD10K, CAMO) demonstrate that ATDMNet establishes a new benchmark in both precision and efficiency.