https://github.com/seung-lab/znn-release
공식 코드Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Papers with Code 아카이브
Boundary Detection
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Boundary Detection is a vital part of extracting information encoded in images, allowing for the computation of quantities of interest including density, velocity, pressure, etc.
Source: A Locally Adapting Technique for Boundary Detection using Image Segmentation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sichengs/manet
공식 코드Manifold-Aware Local Feature Modeling for Semi-Supervised Medical Image Segmentation
https://github.com/silversolver/ai_boundary_detection
공식 코드https://github.com/simonecarnemolla/Word-Segmenter
공식 코드Back to Supervision: Boosting Word Boundary Detection through Frame Classification
https://github.com/sming256/timeloc
공식 코드TimeLoc: A Unified End-to-End Framework for Precise Timestamp Localization in Long Videos
https://github.com/soCzech/TransNet
공식 코드TransNet: A deep network for fast detection of common shot transitions
https://github.com/soCzech/TransNetV2
공식 코드TransNet V2: An effective deep network architecture for fast shot transition detection
https://github.com/stanfordnlp/pdf-struct
공식 코드Capturing Logical Structure of Visually Structured Documents with Multimodal Transition Parser
https://github.com/tanghaoyu258/ACRM-for-moment-retrieval
공식 코드https://github.com/tb2-sy/tsp-transformer
공식 코드TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding
https://github.com/th2l/GEBD-PyramidFeatureSimilarity
공식 코드Generic Event Boundary Detection in Video with Pyramid Features
https://github.com/tobiasbrugger/multilegalsbd
공식 코드MultiLegalSBD: A Multilingual Legal Sentence Boundary Detection Dataset
https://github.com/ttgeng233/LongVALE
공식 코드https://github.com/wang3702/CoSeg
공식 코드CoSeg: Cognitively Inspired Unsupervised Generic Event Segmentation
https://github.com/weiyao1996/ScRoadExtractor
공식 코드https://github.com/wentaozhu/AutoShot
공식 코드AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection
https://github.com/whu-usi3dv/mobile-seed
공식 코드Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots
https://github.com/xavysp/DexiNed
공식 코드Dense Extreme Inception Network: Towards a Robust CNN Model for Edge Detection
https://github.com/xavysp/teed
공식 코드Tiny and Efficient Model for the Edge Detection Generalization
https://github.com/xiaofang007/cto
공식 코드Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation
https://github.com/yl596/otas
공식 코드OTAS: Unsupervised Boundary Detection for Object-Centric Temporal Action Segmentation
https://github.com/yunlong10/CAT-V
공식 코드https://github.com/zhongping-zhang/movie-clip
공식 코드Movie Genre Classification by Language Augmentation and Shot Sampling
https://github.com/ziwei-zheng/efficientgebd
공식 코드Rethinking the Architecture Design for Efficient Generic Event Boundary Detection
https://github.com/965694547/hybrid-system-of-frame-wise-model-and-sedt
Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection
https://github.com/AceCoooool/DSS-pytorch
Deeply supervised salient object detection with short connections
https://github.com/Andrew-Qibin/DSS
Deeply supervised salient object detection with short connections
https://github.com/Animadversio/FloodFillNetwork-Notes
https://github.com/Carlisle-Liu/SBNet
Learning structure-aware semantic segmentation with image-level supervision
https://github.com/Diooooo/Reimplement-HED
https://github.com/EPFL-VILAB/XDEnsembles
https://github.com/JoelLeupp/Conv3D-Shot-Boundary-Detection
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/MikeG112/RFSBD
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks
https://github.com/Mind23-2/MindCode-3/tree/main/hed
https://github.com/MindSpore-paper-code-3/code2/tree/main/DDM
Progressive Attention on Multi-Level Dense Difference Maps for Generic Event Boundary Detection
https://github.com/NVIDIA/nv-wavenet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.