Search Results for author:
Found 6 papers, 3 papers with code
From Parts to Whole: A Unified Reference Framework for Controllable Human Image Generation
Addressing this, we introduce Parts2Whole, a novel framework designed for generating customized portraits from multiple reference images, including pose images and various aspects of human appearance.
Assessment of Multimodal Large Language Models in Alignment with Human Values
Large Language Models (LLMs) aim to serve as versatile assistants aligned with human values, as defined by the principles of being helpful, honest, and harmless (hhh).
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Multi-modal Large Language Models (MLLMs) have shown impressive abilities in generating reasonable responses with respect to multi-modal contents.
ChEF: A Comprehensive Evaluation Framework for Standardized Assessment of Multimodal Large Language Models
We will publicly release all the detailed implementations for further analysis, as well as an easy-to-use modular toolkit for the integration of new recipes and models, so that ChEF can be a growing evaluation framework for the MLLM community.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
Exploring Adversarial Fake Images on Face Manifold
Then, the generated fake images driven by the adversarial latent vectors with the help of GANs can defeat main-stream forensic models.
