https://github.com/junjun-jiang/BaMBNet
공식 코드BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception
Region-aware Adaptive Instance Normalization for Image Harmonization
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
NTU4DRadLM: 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy
Dense RepPoints: Representing Visual Objects with Dense Point Sets
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute Systems
Align and Distill: Unifying and Improving Domain Adaptive Object Detection
Improving Health Question Answering with Reliable and Time-Aware Evidence Retrieval
Diamond in the rough: Improving image realism by traversing the GAN latent space
Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation
Poisoned Forgery Face: Towards Backdoor Attacks on Face Forgery Detection
A Normalized Gaussian Wasserstein Distance for Tiny Object Detection
Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection
InstructDET: Diversifying Referring Object Detection with Generalized Instructions
Deep Learning based Switching Filter for Impulsive Noise Removal in Color Images
Narrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
Boosting Object Representation Learning via Motion and Object Continuity
BioGAN: An unpaired GAN-based image to image translation model for microbiological images
YOLIC: An Efficient Method for Object Localization and Classification on Edge Devices
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.