https://github.com/I2RDL2/ASTAR-3D
공식 코드A*3D Dataset: Towards Autonomous Driving in Challenging Environments
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A*3D Dataset: Towards Autonomous Driving in Challenging Environments
RegionViT: Regional-to-Local Attention for Vision Transformers
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
Recursively Refined R-CNN: Instance Segmentation with Self-RoI Rebalancing
Re-Scoring Using Image-Language Similarity for Few-Shot Object Detection
NMS-Loss: Learning with Non-Maximum Suppression for Crowded Pedestrian Detection
DCA: Dividing and Conquering Amnesia in Incremental Object Detection
WEDGE: A multi-weather autonomous driving dataset built from generative vision-language models
Co-segmentation Inspired Attention Module for Video-based Computer Vision Tasks
Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
What is the Visual Cognition Gap between Humans and Multimodal LLMs?
ALFA: Agglomerative Late Fusion Algorithm for Object Detection
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
GVCCI: Lifelong Learning of Visual Grounding for Language-Guided Robotic Manipulation
MatchPlant: An Open-Source Pipeline for UAV-Based Single-Plant Detection and Data Extraction
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
Weakly-Supervised Spatio-Temporally Grounding Natural Sentence in Video
Network transferability of adversarial patches in real-time object detection
Multi-Scale Aligned Distillation for Low-Resolution Detection
SipMask: Spatial Information Preservation for Fast Image and Video Instance Segmentation
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
GRiT: A Generative Region-to-text Transformer for Object Understanding
Network Decoupling: From Regular to Depthwise Separable Convolutions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.