Paper Reading AI Learner

Global-Local Aggregation with Deformable Point Sampling for Camouflaged Object Detection

2022-11-22 06:43:37
Minhyeok Lee, Suhwan Cho, Chaewon Park, Dogyoon Lee, Jungho Lee, Sangyoun Lee

Abstract

The camouflaged object detection (COD) task aims to find and segment objects that have a color or texture that is very similar to that of the background. Despite the difficulties of the task, COD is attracting attention in medical, lifesaving, and anti-military fields. To overcome the difficulties of COD, we propose a novel global-local aggregation architecture with a deformable point sampling method. Further, we propose a global-local aggregation transformer that integrates an object's global information, background, and boundary local information, which is important in COD tasks. The proposed transformer obtains global information from feature channels and effectively extracts important local information from the subdivided patch using the deformable point sampling method. Accordingly, the model effectively integrates global and local information for camouflaged objects and also shows that important boundary information in COD can be efficiently utilized. Our method is evaluated on three popular datasets and achieves state-of-the-art performance. We prove the effectiveness of the proposed method through comparative experiments.

Abstract (translated)

URL

https://arxiv.org/abs/2211.12048

PDF

https://arxiv.org/pdf/2211.12048.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot