논문 리뷰 (40) 썸네일형 리스트형 [MoH 논문 리뷰] - MULTI-HEAD ATTENTION AS MIXTURE-OF-HEAD ATTENTION *MoH를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! MoH paper: [2410.11842] MoH: Multi-Head Attention as Mixture-of-Head Attention (arxiv.org) MoH: Multi-Head Attention as Mixture-of-Head AttentionIn this work, we upgrade the multi-head attention mechanism, the core of the Transformer model, to improve efficiency while maintaining or surpassing the previous accuracy level. We show that multi-head attentio.. [Dense Connector 논문 리뷰] - Dense Connector for MLLMs *Dense Connector를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! Dense Connector paper: [2405.13800v1] Dense Connector for MLLMs (arxiv.org) Dense Connector for MLLMsDo we fully leverage the potential of visual encoder in Multimodal Large Language Models (MLLMs)? The recent outstanding performance of MLLMs in multimodal understanding has garnered broad attention from both academia and industry. In the curre.. [LLaVA 논문 리뷰] - Visual Instruction Tuning *LLaVA를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LLaVA github: https://llava-vl.github.io/ LLaVABased on the COCO dataset, we interact with language-only GPT-4, and collect 158K unique language-image instruction-following samples in total, including 58K in conversations, 23K in detailed description, and 77k in complex reasoning, respectively. Pleasellava-vl.github.ioContents1. Simple Introduction2. Ba.. [MeshAnything 논문 리뷰] - MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers *MeshAnything를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! MeshAnything paper: https://arxiv.org/abs/2406.10163 MeshAnything: Artist-Created Mesh Generation with Autoregressive TransformersRecently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because thes.. [Mamba 논문 리뷰 5] - Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model *Mamba 논문 리뷰 시리즈5 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaVision Mamba paper: [2401.09417] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model (arxiv.org) Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space ModelRecently the state space models (SSMs) with efficient hardware-aw.. [Mamba 논문 리뷰 1] - HiPPO: Recurrent Memory with Optimal Polynomial Projections *Mamba 논문 리뷰 시리즈1 입니다! 궁금하신 점은 댓글로 남겨주세요!시리즈 1: Hippo시리즈 2: LSSL시리즈 3: S4시리즈 4: Mamba시리즈 5: Vision MambaHiPPO paper: https://arxiv.org/abs/2008.07669 HiPPO: Recurrent Memory with Optimal Polynomial ProjectionsA central problem in learning from sequential data is representing cumulative history in an incremental fashion as more data is processed. We introduce a general framework (HiPPO) for the o.. [VAE 논문 리뷰] - Auto-Encoding Variational Bayes *VAE 수학적 지식을 리뷰하기 글입니다! 궁금하신 점은 댓글로 남겨주세요! *(통계학, 확률론 지식이 있다고 가정합니다.) VAE paper: https://arxiv.org/pdf/1312.6114.pdf Contents 1. Simple Introduction 2. Mathematical Method - Intractable - Variation lower bound - Reparametrization trick Simple Introduction VAE는 컴퓨터 비전 분야에 한 획을 그은 방법론이다. 특히 image generation 분야에서는 엄청나다고 할 수 있다. 요즘은 VAE보다 훨씬 진보된 모델 diffusion이 자리를 아예 잡고 있어서 해당 논문을 이해하지 않는다면 최신 트렌드를 따.. [DAE-Former 논문 리뷰] - DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation *DAE-Former를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! DAE-Former paper: [2212.13504] DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation (arxiv.org) DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image Segmentation Transformers have recently gained attention in the computer vision domain due to their ability to model long-range dependencies. Howev.. [GPT-1 논문 리뷰] - Improving Language Understanding by Generative Pre-Training *GPT-1를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! (학기중이라 블로그를 자주 못 쓰는데.. 나중에 시간되면 ChatGPT도 정리해서 올릴께요. 일단 간단한 GPT부터..ㅎㅎ) GPT-1 paper: https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf Contents 1. Simple Introduction 2. Background Knowledge: Transformer 3. Method - Unsupervised Stage - Supervised Stage 4. Result Simple Introduction 최근에 .. [MCCNet 논문 리뷰] - Arbitrary Video Style Transfer via Multi-Channel Correlation *MCCNet를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! MCCNet paper: [2009.08003] Arbitrary Video Style Transfer via Multi-Channel Correlation (arxiv.org) Arbitrary Video Style Transfer via Multi-Channel Correlation Video style transfer is getting more attention in AI community for its numerous applications such as augmented reality and animation productions. Compared with traditional image style transfer, .. [MPS-Net 논문 리뷰] - Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation from Monocular Video *MPS-Net를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! MPS-Net project page: MPS-Net MPS-Net References [6] Hongsuk Choi, Gyeongsik Moon, Ju Yong Chang, and Kyoung Mu Lee. Beyond static features for temporally consistent 3D human pose and shape from a video. CVPR, 2021. [8] Carl Doersch and Andrew Zisserman. Sim2real transfer learning for 3D human mps-net.github.io MPS-Net github: GitHub - MPS-Net/MPS-Net_.. [StylizedNeRF 논문 리뷰] - StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning *StylizedNeRF를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! StylizedNeRF project page: StylizedNeRF (geometrylearning.com) StylizedNeRF StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning Yi-Hua Huang1,2 Yue He1,2 Yu-Jie Yuan1,2 Yu-Kun Lai3 Lin Gao1,2† --> † Corresponding author 1 Institute of Computing Technology, Chinese Ac geometrylearning.com StylizedNeRF github: Gi.. 이전 1 2 3 4 다음
