https://github.com/alexanderhberger/cross-dim_i2g
공식 코드Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation
Adaptive Bounding Box Uncertainties via Two-Step Conformal Prediction
Cross-View Hierarchy Network for Stereo Image Super-Resolution
Self-Calibrated Efficient Transformer for Lightweight Super-Resolution
EventGAN: Leveraging Large Scale Image Datasets for Event Cameras
BSRT: Improving Burst Super-Resolution with Swin Transformer and Flow-Guided Deformable Alignment
Controlling Vision-Language Models for Multi-Task Image Restoration
Image Restoration with Mean-Reverting Stochastic Differential Equations
Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution
YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6
COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts
Enhance the Visual Representation via Discrete Adversarial Training
ESOD: Efficient Small Object Detection on High-Resolution Images
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Sensitivity of Average Precision to Bounding Box Perturbations
SVBR-NET: A Non-Blind Spatially Varying Defocus Blur Removal Network
Underwater Object Detection Enhancement via Channel Stabilization
Patch-level Representation Learning for Self-supervised Vision Transformers
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
Iranis: A Large-scale Dataset of Farsi License Plate Characters
Mixture Dense Regression for Object Detection and Human Pose Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.