2D Object Detection
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jerpelhan/GeCo
공식 코드A Novel Unified Architecture for Low-Shot Counting by Detection and Segmentation
https://github.com/jerrysunning/drrnet
공식 코드DRRNet: Macro-Micro Feature Fusion and Dual Reverse Refinement for Camouflaged Object Detection
https://github.com/jessiew0806/bi-lrfusion
공식 코드Bi-LRFusion: Bi-Directional LiDAR-Radar Fusion for 3D Dynamic Object Detection
https://github.com/jgiroux8/T_FFTRadNet
공식 코드T-FFTRadNet: Object Detection with Swin Vision Transformers from Raw ADC Radar Signals
https://github.com/jgocm/ssl-detector
공식 코드An Embedded Monocular Vision Approach for Ground-Aware Objects Detection and Position Estimation
https://github.com/jgwak/GSDN
공식 코드Generative Sparse Detection Networks for 3D Single-shot Object Detection
https://github.com/jh-research/multipleteachers
공식 코드https://github.com/jhcho99/coformer
공식 코드Collaborative Transformers for Grounded Situation Recognition
https://github.com/jhcho99/gsrtr
공식 코드https://github.com/jhjie/edgenat
공식 코드https://github.com/jhl-det/rgbd_video_sod
공식 코드ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
https://github.com/jhl-hust/trasw
공식 코드Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackers
https://github.com/jhuang81/weak-sup-visual-grounding
공식 코드Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation
https://github.com/jiadingcn/geminifusion
공식 코드GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer
https://github.com/jiajiali04/semiweeds
공식 코드Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection
https://github.com/jialeli1/from-voxel-to-point
공식 코드From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder
https://github.com/jialeli1/mgaf-3dssd
공식 코드Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud
https://github.com/jiaming-wang/GLSD
공식 코드GLSD: The Global Large-Scale Ship Database and Baseline Evaluations
https://github.com/jianghaojun/CondenseNetV2
공식 코드CondenseNet V2: Sparse Feature Reactivation for Deep Networks
https://github.com/jiangzhengkai/LSTS
공식 코드Learning Where to Focus for Efficient Video Object Detection
https://github.com/jiaojiayuasd/dilateformer
공식 코드DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
https://github.com/jiaweihe1996/ba-det
공식 코드3D Video Object Detection with Learnable Object-Centric Global Optimization
https://github.com/jiaweilian/benchmarking_physical_attack
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jiaweilian/padetbench
공식 코드PADetBench: Towards Benchmarking Physical Attacks against Object Detection
https://github.com/jiaxi-wu/MPSR
공식 코드Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
https://github.com/jiayouqin/h-splitter
공식 코드Motor Focus: Fast Ego-Motion Prediction for Assistive Visual Navigation
https://github.com/jiayuanwang-jw/yolov8-multi-task
공식 코드https://github.com/jiayuzou2020/diffbev
공식 코드DiffBEV: Conditional Diffusion Model for Bird's Eye View Perception
https://github.com/jichengyuan/cooperitive_students
공식 코드Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection
https://github.com/jimas95/CMinMax
공식 코드cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex Polytope
https://github.com/jimmyma99/sarchat
공식 코드SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image Interpretation
https://github.com/jin-s13/unifs
공식 코드UniFS: Universal Few-shot Instance Perception with Point Representations
https://github.com/jinga-lala/damex
공식 코드DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets
https://github.com/jingy1yu/scalablemap
공식 코드ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
https://github.com/jingyangxiang/maxq
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.