Table of Content: 00:00 CNN Summary 00:58 Analogy of CNN with Graph 03:00 Self-loop connection 04:22 GCN paper link: https://arxiv.org/abs/1609.02907 Icon made by Freepik from flaticon.com
List Of Vocabulary With pictures | Vocabulary With Sentence | Daily Use English Words
HAYATTA KALMAK İÇİN SAVAŞ / Remnant 2 Türkçe Multiplayer 2024 - Bölüm 1
*NEW* BEST COMPETITIVE STRIKER BUILD | EAFC 25 Clubs
包子聊天系列 - 与“领offer”创始人聊聊当前湾区面试情况&新鲜Tiktok面经和技巧
Symphony X - Death of Balance/Lacrymosa (Piano Arrangement)
Power Automate - How to convert audio file into text using Azure Blob storage & Azure Speech Service
KIBRIS'IN İLK ALIŞVERİŞ MERKEZİNİ AÇTIM! Mall Simulator 2025
BMW E30 Oil Pan Gasket Replacement (without removing oil pump)
Autoregressive Image Generation without Vector Quantization
Diffusion Models (DDPM & DDIM) - Easily explained!
GLIGEN (CVPR2023): Open-Set Grounded Text-to-Image Generation
The Entropy Enigma: Success and Failure of Entropy Minimization
Tent: Fully Test-time Adaptation by Entropy Minimization
VPD (ICCV2023): Unleashing Text-to-Image Diffusion Models for Visual Perception
TokenHMR (CVPR2024): Advancing Human Mesh Recovery witha Tokenized Pose Representation
SHViT (CVPR2024): Single-Head Vision Transformer with Memory Efficient Macro Design
InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image Generation
FastV: An Image is Worth 1/2 Tokens After Layer 2
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
PoseGPT (ChatPose): Chatting about 3D Human Pose
MotionAGFormer (WACV2024): Enhancing 3D Human Pose Estimation with a Transformer-GCNFormer Network
HD-GCN (ICCV2023): Skeleton-Based Action Recognition
ST-GCN: Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
Graph Convolutional Networks (GCN): From CNN point of view
DINO: Self-Supervised Vision Transformers
MoCo (+ v2): Unsupervised learning in computer vision
ViTPose: 2D Human Pose Estimation
TrackFormer: Multi-Object Tracking with Transformers
MetaFormer is Actually What You Need for Vision
ConvNet beats Vision Transformers (ConvNeXt) Paper explained
Swin Transformer V2 - Paper explained
Masked Autoencoders (MAE) Paper Explained