https://github.com/cmb-chula/recasnet
공식 코드ReCasNet: Improving consistency within the two-stage mitosis detection framework
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ReCasNet: Improving consistency within the two-stage mitosis detection framework
Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss
ViP: Virtual Pooling for Accelerating CNN-based Image Classification and Object Detection
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
Integrating Extra Modality Helps Segmentor Find Camouflaged Objects Well
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object Detection
Comprehensive Analysis of the Object Detection Pipeline on UAVs
Tackling the Background Bias in Sparse Object Detection via Cropped Windows
Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations
A recurrent CNN for online object detection on raw radar frames
Nonlinear optical encoding enabled by recurrent linear scattering
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
OVT-B: A New Large-Scale Benchmark for Open-Vocabulary Multi-Object Tracking
Dr$^2$Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
Among Us: Adversarially Robust Collaborative Perception by Consensus
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
Enhancing Robot Learning through Learned Human-Attention Feature Maps
Class Imbalance in Object Detection: An Experimental Diagnosis and Study of Mitigation Strategies
Getting to Know Low-light Images with The Exclusively Dark Dataset
TongueSAM: An Universal Tongue Segmentation Model Based on SAM with Zero-Shot
What's in the Black Box? The False Negative Mechanisms Inside Object Detectors
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection
VALO: A Versatile Anytime Framework for LiDAR-based Object Detection Deep Neural Networks
ReDet: A Rotation-equivariant Detector for Aerial Object Detection
Expanding Low-Density Latent Regions for Open-Set Object Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.