2 citations · 2 across the 10 of their papers we have counts for
10 papers
FedSceneX: Time-to-Target Orchestration for Same-Scene Multimodal Federated Edge Learning
Dhe Yeong Tchalla, Beining Wu, Jun Huang +2
Federated learning at the sensing edge is typically evaluated by communication rounds, yet a round does not represent a fixed amount of work. Even on identical hardware, the method…
Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models
Yifu Yuan, Yaoting Huang, Xianze Yao +20
We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, cor…
Optimizing Visual Generative Models via Distribution-wise Rewards
Ruihang Li, Mengde Xu, Shuyang Gu +4
Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degr…
Understanding vs. Generation: Navigating Optimization Dilemma in Multimodal Models
Sen Ye, Mengde Xu, Shuyang Gu +3
Current research in multimodal models faces a key challenge where enhancing generative capabilities often comes at the expense of understanding, and vice versa. We analyzed this tr…
Distribution Matching Variational AutoEncoder
Sen Ye, Jianning Pei, Mengde Xu +4
Most visual generative models compress images into a latent space before applying diffusion or autoregressive modelling. Yet, existing approaches such as VAEs and foundation model…
PromptEnhancer: A Simple Approach to Enhance Text-to-Image Models via Chain-of-Thought Prompt Rewriting
Linqing Wang, Ximing Xing, Yiji Cheng +13
Recent advancements in text-to-image (T2I) diffusion models have demonstrated remarkable capabilities in generating high-fidelity images. However, these models often struggle to fa…
X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
Zigang Geng, Yibing Wang, Yeyao Ma +10
Numerous efforts have been made to extend the ``next token prediction'' paradigm to visual contents, aiming to create a unified approach for both image generation and understanding…
Optimal Stepsize for Diffusion Sampling
Jianning Pei, Han Hu, Shuyang Gu
Diffusion models achieve remarkable generation quality but suffer from computational intensive sampling due to suboptimal step discretization. While existing works focus on optimiz…
Tokenize Image as a Set
Zigang Geng, Mengde Xu, Han Hu +1
This paper proposes a fundamentally new paradigm for image generation through set-based tokenization and distribution modeling. Unlike conventional methods that serialize images in…
Several questions of visual generation in 2024
Shuyang Gu
This paper does not propose any new algorithms but instead outlines various problems in the field of visual generation based on the author's personal understanding. The core of the…