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[CVPR 2024 🔥] Grounding Large Multimodal Model (GLaMM), the first-of-its-kind model capable of generating natural language responses that are seamlessly integrated with object segmentation masks.
Implementation for "Correcting Diffusion Generation through Resampling" [CVPR 2024]
Code for the paper "Training Diffusion Models with Reinforcement Learning"
[NeurIPS'23 Spotlight] Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
[CVPR2024] DisCo: Referring Human Dance Generation in Real World
VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models
harubaru / waifu-diffusion
Forked from CompVis/stable-diffusionstable diffusion finetuned on weeb stuff
PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
GLIDE: a diffusion-based text-conditional image synthesis model
🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch and FLAX.
Refine high-quality datasets and visual AI models
ML model optimization product to accelerate inference.
Top-level directory for documentation and general content
Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes
[ICCV 2023] Prompt-aligned Gradient for Prompt Tuning
The official code of CVPR 2022 paper (Class Re-Activation Maps for Weakly-Supervised Semantic Segmentation).
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
Unofficial PyTorch Reimplementation of AutoAugment and RandAugment.
Unofficial PyTorch Reimplementation of RandAugment.
A comprehensive list of awesome contrastive self-supervised learning papers.
[CVPR 2021] Counterfactual VQA: A Cause-Effect Look at Language Bias
[CVPR 2021] Released code for Counterfactual Zero-Shot and Open-Set Visual Recognition
One-of-a-kind Complaint Handling Dataset
The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919
更新2008年版本的《上海交通大学生存手册》gitbook发布于https://survivesjtu.gitbook.io/survivesjtumanual/
PyTorch implementation of "An Ensemble of Epoch-wise Empirical Bayes for Few-shot Learning" (ECCV 2020)