lgzlIlIlI

lgzlIlIlI's Stars

• datawhalechina/so-large-lm

  大模型基础: 一文了解大模型基础知识

  2.9k261

• liguodongiot/llm-action

  本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）

  Language:HTML10.4k1k

• Run542968/GAP

  Language:Python6

• damNull/LAGCN

  Language:Python457

• Tangkfan/Temporal-Sentence-Grounding

  paper list on Video Moment Retrieval (VMR), or Natural Language Video Localization (NLVL), Video Grounding (VG), or Temporal Sentence Grounding in Videos (TSGV)

  3

• amusi/Computer-Vision-Tasks-Survey

  【CVer出品】旨在盘点最全面的计算机视觉方向

  331

• zhanglj37/Tutorial-on-PhD-Application

  Tutorial on PhD Application

  871102

• williamium3000/awesome-mllm-grounding

  Awesome paper for multi-modal llm with grounding ability

  111

• sunoh-kim/pps

  Pytorch implementation of the paper 'Gaussian Mixture Proposals with Pull-Push Learning Scheme to Capture Diverse Events for Weakly Supervised Temporal Video Grounding' (AAAI2024).

  Language:Python13

• yyyujintang/Awesome-Mamba-Papers

  Awesome Papers related to Mamba.

  1.2k63

• sming256/ETAD

  [CVPRW2023] The official implementation of ETAD: A Unified Framework for Efficient Temporal Action Detection

  Language:Python18

• sming256/AdaTAD

  [CVPR2024] The official implementation of AdaTAD: End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames

  321

• jackfrued/Python-100-Days

  Python - 100天从新手到大师

  Language:Python157k52.3k

• scutan90/DeepLearning-500-questions

  深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系scutjy2015@163.com 版权所有，违权必究 Tan 2018.06

  Language:JavaScript54.8k15.9k

• Raymond-sci/EMB

  Pytorch Implementation of ECCV'22 paper: Video Activity Localisation with Uncertainties in Temporal Boundary

  Language:Python15

• YYJMJC/Compositional-Temporal-Grounding

  301

• JunweiLiang/awesome_lists

  Awesome Lists for Tenure-Track Assistant Professors and PhD students. (助理教授/博士生生存指南)

  Language:Python1.5k81

• ChaoningZhang/MobileSAM

  This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!

  Language:Jupyter Notebook4.8k501

• BradyFU/Awesome-Multimodal-Large-Language-Models

  :sparkles::sparkles:Latest Advances on Multimodal Large Language Models

  12.6k803

• sb2nov/resume

  Software developer resume in Latex

  Language:TeX5.3k1.5k

• MengyuanChen21/CVPR2023-CMPAE

  [CVPR 2023] Collecting Cross-Modal Presence-Absence Evidence for Weakly-Supervised Audio-Visual Event Perception

  Language:Python343

• labmlai/annotated_deep_learning_paper_implementations

  🧑‍🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

  Language:Python56.1k5.8k

• IDEA-Research/Grounded-Segment-Anything

  Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

  Language:Jupyter Notebook15.1k1.4k

• OpenGVLab/InternVideo

  [ECCV2024] Video Foundation Models & Data for Multimodal Understanding

  Language:Python1.4k85

• DAMO-NLP-SG/Video-LLaMA

  [EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding

  Language:Python2.8k257

• zhou745/GauFuse_WSTAL

  Language:Python213

• ttengwang/Awesome_Prompting_Papers_in_Computer_Vision

  A curated list of prompt-based paper in computer vision and vision-language learning.

  89470

• binary-husky/gpt_academic

  为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。

  Language:Python65.5k8.1k

• pengsida/learning_research

  本人的科研经验

  5.9k346

• facebookresearch/segment-anything

  The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

  Language:Jupyter Notebook47.5k5.6k