https://github.com/dc3234/pf
공식 코드A positive feedback method based on F-measure value for Salient Object Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A positive feedback method based on F-measure value for Salient Object Detection
Discriminative Co-Saliency and Background Mining Transformer for Co-Salient Object Detection
Recurrent Multi-scale Transformer for High-Resolution Salient Object Detection
Lightweight RGB-D Salient Object Detection from a Speed-Accuracy Tradeoff Perspective
Addressing Multiple Salient Object Detection via Dual-Space Long-Range Dependencies
Group Collaborative Learning for Co-Salient Object Detection
LF Tracy: A Unified Single-Pipeline Approach for Salient Object Detection in Light Field Cameras
Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
Generative Transformer for Accurate and Reliable Salient Object Detection
Generative Transformer for Accurate and Reliable Salient Object Detection
Seg-R1: Segmentation Can Be Surprisingly Simple with Reinforcement Learning
Hyperspectral Image Dataset for Benchmarking on Salient Object Detection
Bi-directional Object-context Prioritization Learning for Saliency Ranking
PEEKABOO: Hiding parts of an image for unsupervised object localization
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Inner and Inter Label Propagation: Salient Object Detection in the Wild
Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
Deep Fourier-embedded Network for Bi-modal Salient Object Detection
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
Light Field Salient Object Detection: A Review and Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.