https://github.com/shaoqb/multi_scale_booster
공식 코드Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster
Papers with Code 아카이브
Object Detection
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/shaoqing26/fusionpainting
공식 코드FusionPainting: Multimodal Fusion with Adaptive Attention for 3D Object Detection
https://github.com/sharat29ag/CDAL
공식 코드https://github.com/sharpiless/cascadev-det
공식 코드https://github.com/shashankskagnihotri/benchmarking_reliability_generalization
공식 코드SemSegBench & DetecBench: Benchmarking Reliability and Generalization Beyond Classification
https://github.com/shaunyuan22/cfinet
공식 코드Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning
https://github.com/sheepooo/pit-position-invariant-transform
공식 코드PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation
https://github.com/shen-aoyu/CREST
공식 코드https://github.com/shengcao-cao/cmt
공식 코드Contrastive Mean Teacher for Domain Adaptive Object Detectors
https://github.com/shengcao-cao/hassod
공식 코드HASSOD: Hierarchical Adaptive Self-Supervised Object Detection
https://github.com/shengyuhao/divotrack
공식 코드https://github.com/shenyi0220/cp-cluster
공식 코드Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
https://github.com/shenyunhang/ape
공식 코드Aligning and Prompting Everything All at Once for Universal Visual Perception
https://github.com/shenyunhang/huwsod
공식 코드HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection
https://github.com/shenzheng2000/tpsence
공식 코드TPSeNCE: Towards Artifact-Free Realistic Rain Generation for Deraining and Object Detection in Rain
https://github.com/shi-labs/natten
공식 코드https://github.com/shichaoy/cube_slam
공식 코드https://github.com/shihe98/hcb
공식 코드Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense
https://github.com/shihuahuang95/deim
공식 코드https://github.com/shijieS/SST
공식 코드https://github.com/shikras/d-cube
공식 코드Described Object Detection: Liberating Object Detection with Flexible Expressions
https://github.com/shinya7y/UniverseNet
공식 코드https://github.com/shipra25jain/ESSNet
공식 코드Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
https://github.com/shiqiu0419/pnp-3d
공식 코드https://github.com/shlokk/hcl
공식 코드Hyperbolic Contrastive Learning for Visual Representations beyond Objects
https://github.com/shlokk/object-cropping-ssl
공식 코드https://github.com/shngjz/wildcamera
공식 코드Tame a Wild Camera: In-the-Wild Monocular Camera Calibration
https://github.com/shoufachen/diffusiondet
공식 코드https://github.com/showlab/efficient-cls
공식 코드Label-Efficient Online Continual Object Detection in Streaming Video
https://github.com/shreyasrajesh/DA-Object-Detection
공식 코드Domain Adaptation for Object Detection using SE Adaptors and Center Loss
https://github.com/shrutarv/mtevent_toolkit
공식 코드MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
https://github.com/shubha07m/On-device-computer-vision-experiments-with-IoT
공식 코드MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO
https://github.com/shubhamagarwal12/warlearn
공식 코드https://github.com/shuyonggao/psod
공식 코드Weakly-Supervised Salient Object Detection Using Point Supervision
https://github.com/shwetabhardwaj44/RecoveringFrom_RandomPruning_WACV2018
공식 코드Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks
https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.