# Curiosity-Driven Camouflaged Object Segmentation
## 1 Introduction  
Camouflaged object segmentation (COS) [1] is the task of extracting objects that are harmoniously blended with their ambient surroundings. Camouflaged objects are ubiquitous in nature and human society, including polyps in the medical field [2, 3, 4, 5], defects in the industrial field [6, 7], objects blurred by bad weather or light in the transportation field [8], and artistic creation objects in the entertainment and art fields [9]. These objects are difficult to identify due to their high degree of integration with the background, requiring more accurate location and more complete object segmentation. Therefore, COS has received increasing attention due to its application and scientific value. The intricate backgrounds of objects and the inherent difficulty in recognizing boundaries pose significant challenges in the field of COS. Early methods aim to exploit hand-crafted low-level features (e.g., color [10], orientation [11], texture [12], 3D convexity [13], motion boundaries [14], etc. ), but the effectiveness of these features in achieving accurate discrimination remains limited. Recently, the progressive advancement of deep learning techniques, coupled with the accessibility of large-scale COS datasets (e.g., COD10K [15], NC4K [16], CAMO [17]), has yielded substantial improvements in the overall segmentationperformance of camouflaged objects. Numerous CNN-based approaches [15; 16; 18; 19; 20; 21; 22] have been proposed to address the COS challenge. Considering the superiority of Transformer in modeling long-term dependencies, many transformer-based methods [23; 24; 25; 26] have also yielded commendable performance outcomes. However, their efficacy may diminish considerably in more intricate scenarios characterized by small targets, multiple targets, and blurred boundaries. As shown in Figure 1, recently proposed CNN-based methods (e.g., FEDER [22]) and Transformer-based methods (e.g., HitNet [25]) both perform poorly in capturing complete and intricate details. In response to this challenge, curiosity, an important driving force for human exploration of the unknown world, can help us solve it. According to the cognitive processes of the human brain, when people encounter ambiguous entities or spatial regions, they naturally tend to exhibit curiosity and are more willing to allocate additional energy to observing and analyzing the object, thereby facilitating a gradual process of perception and confirmation. Inspired by this, we interpret curiosity as the model's drive and inclination to explore unknown or subtle information, particularly those features that are challenging to discern. When encountering camouflaged objects, the model's curiosity is stimulated by the concealment of their appearance and texture, prompting active exploration and learning of these object features. Based on this idea, we propose a novel Curiosity-Driven network (CDNet) to promote the model's learning and explo  
Figure 1: Visual comparison of COS in different challenging scenarios, including large objects, small objects, multiple objects, occluded objects, and objects with background matching (from top to bottom in the figure). The local heatmap is placed in the bottom right corner. Compared with the recently proposed CNN-based method FEDER [22] and Transformer-based HitNet [25], our method provides superior performance with more accurate object localization and more complete object segmentation, mainly due to the proposed fusion bridge module and curiosity-refinement module. ing the model's curiosity to facilitate a more effective segmentation process. Our CDNet contains three essential modules, i.e., the dual-branch feature encoder (DFE), the fusion bridge module (FBM), and the curiosity-refinement module (CRM). The DFE is designed to extract multi-level global features from the Transformer and local features from CNN. Subsequently, the FBM skillfully integrates these global and local features, harnessing curiosity to facilitate the fusion process.