https://github.com/jxr326/swinmcnet
A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
Source: An Unsupervised Game-Theoretic Approach to Saliency Detection
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Unified RGB-T Saliency Detection Benchmark: Dataset, Baselines, Analysis and A Novel Approach
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Adaptive Graph Convolutional Network with Attention Graph Clustering for Co-saliency Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Deeply supervised salient object detection with short connections
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Contextual Encoder-Decoder Network for Visual Saliency Prediction
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Light Field Saliency Detection with Deep Convolutional Networks
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM
Fast Underwater Image Enhancement for Improved Visual Perception
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Advancing Referring Expression Segmentation Beyond Single Image
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Faster gaze prediction with dense networks and Fisher pruning
U$^2$-Net: Going Deeper with Nested U-Structure for Salient Object Detection
Visual Saliency Detection Based on Multiscale Deep CNN Features
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.