Stars
Official and maintained implementation of the paper "Differentiable JPEG: The Devil is in the Details" [WACV 2024].
Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.
A list of recent papers about adversarial learning
Janus-Series: Unified Multimodal Understanding and Generation Models
📖 A curated list of resources dedicated to hallucination of multimodal large language models (MLLM).
🔥🔥🔥Latest Papers, Codes and Datasets on Vid-LLMs.
List of papers on hallucination detection in LLMs.
[ICCV 2023 Oral] Official implementation of "Robust Evaluation of Diffusion-Based Adversarial Purification"
Image Forgery Detection and Localization (and related) Papers List
State-of-the-Art Text Embeddings
Collection of AWESOME vision-language models for vision tasks
A reading list for large models safety, security, and privacy (including Awesome LLM Security, Safety, etc.).
Offical implement of NCL-IML (Pre-training-free Image Manipulation Localization through Non-Mutually Contrastive Learning), ICCV2023
Strong baselines for tampered text detection in pure vision domain
[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.
2022阿里天池真实场景篡改图像检测挑战赛-冠军方案(1/1149)
[NeurIPS 2023] Codes for DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purification
An unrestricted attack based on diffusion models that can achieve both good transferability and imperceptibility.
[NeurIPS 2024] MoVA: Adapting Mixture of Vision Experts to Multimodal Context
Official pytorch implementation of "AIGCs Confuse AI Too: Investigating and Explaining Synthetic Image-induced Hallucinations in Large Vision-Language Models"
Accelerating the development of large multimodal models (LMMs) with one-click evaluation module - lmms-eval.
Eagle Family: Exploring Model Designs, Data Recipes and Training Strategies for Frontier-Class Multimodal LLMs
【三年面试五年模拟】AI算法工程师面试秘籍。涵盖AIGC、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、强化学习、具身智能、元宇宙、AGI等AI行业面试笔试经验与干货知识。
CVPR 2022, Robust Contrastive Learning against Noisy Views
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples