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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tensorflow/models/tree/master/official/vision/beta/modeling/backbones

공식 코드

SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization

https://github.com/tensorflow/models/tree/master/research/lstm_object_detection

공식 코드

Looking Fast and Slow: Memory-Guided Mobile Video Object Detection

https://github.com/tensorflow/tensorflow/blob/v2.4.1/tensorflow/python/keras/applications/mobilenet.py

공식 코드

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/tensorflow/tpu

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MnasNet: Platform-Aware Neural Architecture Search for Mobile

https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/copy_paste

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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation

https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/vild

공식 코드

Open-vocabulary Object Detection via Vision and Language Knowledge Distillation

https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet

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EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/terry9a/dsrc

공식 코드

DSRC: Learning Density-insensitive and Semantic-aware Collaborative Representation against Corruptions

https://github.com/teryzh/reconstruction-ngsim-trajectory-with-dmd-and-res_unet_plus

공식 코드

Spatial-Temporal Map Vehicle Trajectory Detection Using Dynamic Mode Decomposition and Res-UNet+ Neural Networks

https://github.com/teryzh/roadside-lidar-vehicle-detection-and-tracking-background-subtraction

공식 코드

Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction

https://github.com/test-time-training/ttt-lm-pytorch

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Learning to (Learn at Test Time): RNNs with Expressive Hidden States

https://github.com/texasinstruments/edgeai-yolov5

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YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss

https://github.com/texasinstruments/edgeai-yolox

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YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss

https://github.com/tfwu/FaceDetection-ConvNet-3D

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Face Detection with End-to-End Integration of a ConvNet and a 3D Model

https://github.com/tgisaturday/L-Verse

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L-Verse: Bidirectional Generation Between Image and Text

https://github.com/tgxs002/cora

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CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching

https://github.com/thangvubk/Cascade-RPN

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Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution

https://github.com/thangvubk/softgroup

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SoftGroup for 3D Instance Segmentation on Point Clouds

https://github.com/theimad/cricket-image-segmentation

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Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket

https://github.com/thensib/repvgg-gelan

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RepVGG-GELAN: Enhanced GELAN with VGG-STYLE ConvNets for Brain Tumour Detection

https://github.com/theodore-pku/theorem-1-verification-python

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Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer

https://github.com/theophil-trippe/HDC_TUBerlin_version_1

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Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images

https://github.com/therebellll/NegIoU-PosIoU-Miou

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Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection

https://github.com/therock90421/da-ada

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DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection

https://github.com/thesstefan/pixlens

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PixLens: A Novel Framework for Disentangled Evaluation in Diffusion-Based Image Editing with Object Detection + SAM

https://github.com/thinklab-sjtu/twns

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Ternary Weight Networks

https://github.com/thograce/C2FNet

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Context-aware Cross-level Fusion Network for Camouflaged Object Detection

https://github.com/thograce/bgnet

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Boundary-Guided Camouflaged Object Detection

https://github.com/thomasjo/nemo-redux

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Instance Segmentation of Microscopic Foraminifera

https://github.com/thsant/add256-fastercnn

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A methodology for detection and localization of fruits in apples orchards from aerial images

https://github.com/thu-mig/yolo-uniow

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YOLO-UniOW: Efficient Universal Open-World Object Detection

https://github.com/thu-ml/imagenet-a-plus

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Rethinking Natural Adversarial Examples for Classification Models

https://github.com/thu-ml/oddefense

공식 코드

On the Importance of Backbone to the Adversarial Robustness of Object Detectors

https://github.com/thu17cyz/3DIoUMatch

공식 코드

3DIoUMatch: Leveraging IoU Prediction for Semi-Supervised 3D Object Detection

https://github.com/thuan-researcher/intruder-thermal-dataset

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PDIWS: Thermal Imaging Dataset for Person Detection in Intrusion Warning Systems

https://github.com/thudm/longbench

공식 코드

LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

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