Search Results for author:
Found 9 papers, 5 papers with code
An Empirical Study of GPT-4o Image Generation Capabilities
The landscape of image generation has rapidly evolved, from early GAN-based approaches to diffusion models and, most recently, to unified generative architectures that seek to bridge understanding and generation tasks.
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
While Vision-Language Models (VLMs) have shown great promise in reasoning and task planning for embodied agents, their ability to comprehend physical phenomena remains extremely limited.
KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
Graph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years.
HumanEdit: A High-Quality Human-Rewarded Dataset for Instruction-based Image Editing
HumanEdit bridges this gap by employing human annotators to construct data pairs and administrators to provide feedback.
AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
Instruction-based image editing aims to modify specific image elements with natural language instructions.
Unified Generative and Discriminative Training for Multi-modal Large Language Models
Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation.
Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis
We present Meissonic, which elevates non-autoregressive masked image modeling (MIM) text-to-image to a level comparable with state-of-the-art diffusion models like SDXL.
Auto-Encoding Morph-Tokens for Multimodal LLM
For multimodal LLMs, the synergy of visual comprehension (textual output) and generation (visual output) presents an ongoing challenge.
Enhancing Cross-domain Link Prediction via Evolution Process Modeling
Extensive experiments on eight untrained graphs demonstrate that DyExpert achieves state-of-the-art performance in cross-domain link prediction.
