https://github.com/iVMCL/AOGNets
공식 코드AOGNets: Compositional Grammatical Architectures for Deep Learning
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AOGNets: Compositional Grammatical Architectures for Deep Learning
HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge Understanding
Semantic Perceptual Image Compression using Deep Convolution Networks
TableFormer: Table Structure Understanding with Transformers
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-rays
Multi-scale Attention Network for Single Image Super-Resolution
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution
E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Pyramid Grafting Network for One-Stage High Resolution Saliency Detection
DINO-X: A Unified Vision Model for Open-World Object Detection and Understanding
DN-DETR: Accelerate DETR Training by Introducing Query DeNoising
DQ-DETR: Dual Query Detection Transformer for Phrase Extraction and Grounding
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
Lite DETR : An Interleaved Multi-Scale Encoder for Efficient DETR
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
T-Rex2: Towards Generic Object Detection via Text-Visual Prompt Synergy
Lightweight and Efficient Image Super-Resolution with Block State-based Recursive Network
Active Object Detection with Knowledge Aggregation and Distillation from Large Models
Sebica: Lightweight Spatial and Efficient Bidirectional Channel Attention Super Resolution Network
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
DR Loss: Improving Object Detection by Distributional Ranking
Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition
A Review of Uncertainty Calibration in Pretrained Object Detectors
A Lightweight Image Super-Resolution Transformer Trained on Low-Resolution Images Only
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.