https://github.com/wuhanstudio/whitebox-adversarial-toolbox
공식 코드A Human-in-the-Middle Attack against Object Detection Systems
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
A Human-in-the-Middle Attack against Object Detection Systems
MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model
Multi-Faceted Distillation of Base-Novel Commonality for Few-shot Object Detection
CLIM: Contrastive Language-Image Mosaic for Region Representation
CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction
Aligning Bag of Regions for Open-Vocabulary Object Detection
Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning
Hypernetworks build Implicit Neural Representations of Sounds
Zero-shot Nuclei Detection via Visual-Language Pre-trained Models
Rethinking Classification and Localization for Object Detection
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label Enhancement
SinSR: Diffusion-Based Image Super-Resolution in a Single Step
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
GAN Prior based Null-Space Learning for Consistent Super-Resolution
Adaptive Distillation: Aggregating Knowledge from Multiple Paths for Efficient Distillation
Paint Transformer: Feed Forward Neural Painting with Stroke Prediction
Eliminating Position Bias of Language Models: A Mechanistic Approach
FPT: Feature Prompt Tuning for Few-shot Readability Assessment
Hardness-Aware Scene Synthesis for Semi-Supervised 3D Object Detection
SVAM: Saliency-guided Visual Attention Modeling by Autonomous Underwater Robots
Fast Underwater Image Enhancement for Improved Visual Perception
Underwater Image Super-Resolution using Deep Residual Multipliers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.