https://github.com/harveymannering/text-to-image-bias
공식 코드Analysing Gender Bias in Text-to-Image Models using Object Detection
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Analysing Gender Bias in Text-to-Image Models using Object Detection
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Adversarial Pixel Restoration as a Pretext Task for Transferable Perturbations
Towards Evaluating the Robustness of Visual State Space Models
PEEKABOO: Hiding parts of an image for unsupervised object localization
Towards unconstrained joint hand-object reconstruction from RGB videos
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Learning from Children: Improving Image-Caption Pretraining via Curriculum
FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis
Few-Cost Salient Object Detection with Adversarial-Paced Learning
Cross-Modal Causal Intervention for Medical Report Generation
Structured Semantic Transfer for Multi-Label Recognition with Partial Labels
Unbalanced Optimal Transport: A Unified Framework for Object Detection
Diffusion Domain Teacher: Diffusion Guided Domain Adaptive Object Detector
On the Robustness of Object Detection Models on Aerial Images
Squeezed Deep 6DoF Object Detection Using Knowledge Distillation
HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information
MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection
Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge
Multimodal Object Detection via Probabilistic a priori Information Integration
Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation
Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization
Boosting Convolutional Neural Networks with Middle Spectrum Grouped Convolution
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods
Level-S$^2$fM: Structure from Motion on Neural Level Set of Implicit Surfaces
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.