AI/Paper - Theory (70) 썸네일형 리스트형 [CogVideoX 논문 리뷰] - Text-to-Video Diffusion Models with An Expert Transformer *CogVideoX를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! CogVideoX paper: https://arxiv.org/abs/2408.06072 CogVideoX: Text-to-Video Diffusion Models with An Expert TransformerWe present CogVideoX, a large-scale text-to-video generation model based on diffusion transformer, which can generate 10-second continuous videos aligned with text prompt, with a frame rate of 16 fps and resolution of 768 * 1360 pixel.. [TransPixar 논문 리뷰] - Advancing Text-to-Video Generation with Transparency *TransPixar를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! TransPixar paper: [2501.03006] TransPixar: Advancing Text-to-Video Generation with Transparency TransPixar: Advancing Text-to-Video Generation with TransparencyText-to-video generative models have made significant strides, enabling diverse applications in entertainment, advertising, and education. However, generating RGBA video, which includes alph.. [SigLip 논문 리뷰] - Sigmoid Loss for Language Image Pre-Training *SigLip를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! SigLip paper: https://arxiv.org/abs/2303.15343 Sigmoid Loss for Language Image Pre-TrainingWe propose a simple pairwise Sigmoid loss for Language-Image Pre-training (SigLIP). Unlike standard contrastive learning with softmax normalization, the sigmoid loss operates solely on image-text pairs and does not require a global view of the pairwise simarxiv.or.. [Skip-DiT 논문 리뷰] - Accelerating Vision Diffusion Transformers with Skip Branches *Skip-DiT를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! Skip-DiT paper: https://arxiv.org/abs/2411.17616 Accelerating Vision Diffusion Transformers with Skip BranchesDiffusion Transformers (DiT), an emerging image and video generation model architecture, has demonstrated great potential because of its high generation quality and scalability properties. Despite the impressive performance, its practical depl.. [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-Video 논문 리뷰] - VIDEO INSTRUCTION TUNING WITH SYNTHETIC DATA *LLaVA-Video를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LLaVA-Video paper: https://arxiv.org/abs/2410.02713 Video Instruction Tuning With Synthetic DataThe development of video large multimodal models (LMMs) has been hindered by the difficulty of curating large amounts of high-quality raw data from the web. To address this, we propose an alternative approach by creating a high-quality synthetic dataset .. [LLaVA-OneVision 논문 리뷰] - LLaVA-OneVision: Easy Visual Task Transfer *LLaVA-OneVision를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LLaVA-OneVision paper: https://arxiv.org/abs/2408.03326 LLaVA-OneVision: Easy Visual Task TransferWe present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series. Our experimental results demonstrate that LLaVA-OneVisi.. [LLaVA-NeXT 논문 리뷰] - Improved Baselines with Visual Instruction Tuning *LLaVA-NeXT를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LLaVA-Next Github: https://github.com/LLaVA-VL/LLaVA-NeXT GitHub - LLaVA-VL/LLaVA-NeXTContribute to LLaVA-VL/LLaVA-NeXT development by creating an account on GitHub.github.com LLaVA-1.5 paper: https://arxiv.org/abs/2310.03744LLaVA-Next (1.6) blog: https://llava-vl.github.io/blog/2024-01-30-llava-next/Contents1. Simple Introduction2. Background Knowl.. [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.. 이전 1 2 3 4 ··· 6 다음
