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2D Classification

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

https://github.com/minwoosun/biomedica-etl

공식 코드

BIOMEDICA: An Open Biomedical Image-Caption Archive, Dataset, and Vision-Language Models Derived from Scientific Literature

https://github.com/miquel-espinosa/no-time-to-train

공식 코드

No time to train! Training-Free Reference-Based Instance Segmentation

https://github.com/miraclehh/cba

공식 코드

Composite Backdoor Attacks Against Large Language Models

https://github.com/mireshghallah/style-pooling

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Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness

https://github.com/mirevi/face-synthesizer-jvrb

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Towards a Pipeline for Real-Time Visualization of Faces for VR-based Telepresence and Live Broadcasting Utilizing Neural Rendering

https://github.com/mirzaalimm/truncationvssummarization

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Investigating Text Shortening Strategy in BERT: Truncation vs Summarization

https://github.com/mit-han-lab/bevfusion

공식 코드

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

https://github.com/mit-han-lab/efficientvit

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EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction

https://github.com/mit-han-lab/pvcnn

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Point-Voxel CNN for Efficient 3D Deep Learning

https://github.com/mit-han-lab/spvnas

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Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

https://github.com/mitscha/strassennets

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StrassenNets: Deep Learning with a Multiplication Budget

https://github.com/miu200521358/NeuralVoicePuppetryMMD

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Neural Voice Puppetry: Audio-driven Facial Reenactment

https://github.com/mivg/sled

공식 코드

Efficient Long-Text Understanding with Short-Text Models

https://github.com/mizvol/WikiBrain

공식 코드

Wikipedia graph mining: dynamic structure of collective memory

https://github.com/mjseong0414/CRT-Fusion

공식 코드

CRT-Fusion: Camera, Radar, Temporal Fusion Using Motion Information for 3D Object Detection

https://github.com/mjseong0414/RCM-Fusion

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RCM-Fusion: Radar-Camera Multi-Level Fusion for 3D Object Detection

https://github.com/mk-minchul/attend-and-compare

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Learning Visual Context by Comparison

https://github.com/mkang315/CST-YOLO

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CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer

https://github.com/mkang315/bgf-yolo

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BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection

https://github.com/mkang315/pk-yolo

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PK-YOLO: Pretrained Knowledge Guided YOLO for Brain Tumor Detection in Multiplanar MRI Slices

https://github.com/mkang315/rcs-yolo

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RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection

https://github.com/mkchoi-0323/modified_refinedet

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Development of Fast Refinement Detectors on AI Edge Platforms

https://github.com/ml-lab-htw/iterative-annotate

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Feedback-driven object detection and iterative model improvement

https://github.com/ml-research/xil

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Making deep neural networks right for the right scientific reasons by interacting with their explanations

https://github.com/mlai-yonsei/exceptionalbenchmark

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Benchmarking Foundation Models on Exceptional Cases: Dataset Creation and Validation

https://github.com/mlcaepiee/simuav

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A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive Fusion

https://github.com/mlpc-ucsd/CoaT

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Co-Scale Conv-Attentional Image Transformers

https://github.com/mlvlab/RALF

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Retrieval-Augmented Open-Vocabulary Object Detection

https://github.com/mlvlab/cpchoi

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Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection

https://github.com/mlvlab/davi

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Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems

https://github.com/mlvlab/drone_ai_challenge

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Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/drone_task1

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Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/drone_task4

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Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlvlab/rony2_task3

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Improving Object Detection, Multi-object Tracking, and Re-Identification for Disaster Response Drones

https://github.com/mlzxy/devit

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Detect Everything with Few Examples

https://github.com/mmaaz60/EdgeNeXt

공식 코드

EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.