https://github.com/vibashan/irg-sfda
공식 코드Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
Attaining Real-Time Super-Resolution for Microscopic Images Using GAN
Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis
CLIP the Gap: A Single Domain Generalization Approach for Object Detection
Learning Transformations To Reduce the Geometric Shift in Object Detection
Towards a Robust Sensor Fusion Step for 3D Object Detection on Corrupted Data
OD3: Optimization-free Dataset Distillation for Object Detection
Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation
SODAWideNet++: Combining Attention and Convolutions for Salient Object Detection
PCRNet: Point Cloud Registration Network using PointNet Encoding
PCRNet: Point Cloud Registration Network using PointNet Encoding
PCRNet: Point Cloud Registration Network using PointNet Encoding
One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution
LiFT: Lightweight, FPGA-tailored 3D object detection based on LiDAR data
Detection-segmentation convolutional neural network for autonomous vehicle perception
PointPillars Backbone Type Selection For Fast and Accurate LiDAR Object Detection
QuadMamba: Learning Quadtree-based Selective Scan for Visual State Space Model
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
Multiple Object Stitching for Unsupervised Representation Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.