7 papers
Corruption-Aware Training of Latent Video Diffusion Models for Robust Text-to-Video Generation
Chika Maduabuchi, Hao Chen, Yujin Han +1
Latent Video Diffusion Models (LVDMs) have achieved state-of-the-art generative quality for image and video generation; however, they remain brittle under noisy conditioning, where…
Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
Jiaxi Yang, Mengqi Zhang, Yiqiao Jin +8
Large Language Model-based Multi-Agent Systems (MASs) have emerged as a powerful paradigm for tackling complex tasks through collaborative intelligence. However, the topology of th…
Masked Autoencoders Are Effective Tokenizers for Diffusion Models
Hao Chen, Yujin Han, Fangyi Chen +7
Recent advances in latent diffusion models have demonstrated their effectiveness for high-resolution image synthesis. However, the properties of the latent space from tokenizer for…
SoftVQ-VAE: Efficient 1-Dimensional Continuous Tokenizer
Hao Chen, Ze Wang, Xiang Li +7
Efficient image tokenization with high compression ratios remains a critical challenge for training generative models. We present SoftVQ-VAE, a continuous image tokenizer that leve…
Realistic Evaluation of Deep Partial-Label Learning Algorithms
Wei Wang, Dong-Dong Wu, Jindong Wang +3
Partial-label learning (PLL) is a weakly supervised learning problem in which each example is associated with multiple candidate labels and only one is the true label. In recent ye…
On the robustness of multimodal language model towards distractions
Ming Liu, Hao Chen, Jindong Wang +1
Although vision-language models (VLMs) have achieved significant success in various applications such as visual question answering, their resilience to prompt variations remains an…