most citedInference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

3 citations · 4 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2025

Next-Embedding Prediction Makes Strong Vision Learners

Sihan Xu, Ziqiao Ma, Wenhao Chai +5

Inspired by the success of generative pretraining in natural language, we ask whether the same principles can yield strong self-supervised visual learners. Instead of training mode…

cs.LG2025

FrontierCS: Evolving Challenges for Evolving Intelligence

Qiuyang Mang, Wenhao Chai, Zhifei Li +48

We introduce FrontierCS, a benchmark of 156 open-ended problems across diverse areas of computer science, designed and reviewed by experts, including CS PhDs and top-tier competiti…

cs.CV2025

BlenderFusion: 3D-Grounded Visual Editing and Generative Compositing

Jiacheng Chen, Ramin Mehran, Xuhui Jia +2

We present BlenderFusion, a generative visual compositing framework that synthesizes new scenes by recomposing objects, camera, and background. It follows a layering-editing-compos…

cs.SE2025

LiveCodeBench Pro: How Do Olympiad Medalists Judge LLMs in Competitive Programming?

Zihan Zheng, Zerui Cheng, Zeyu Shen +16

Recent reports claim that large language models (LLMs) now outperform elite humans in competitive programming. Drawing on knowledge from a group of medalists in international algor…

cs.CV2025★ 1 cited

Science-T2I: Addressing Scientific Illusions in Image Synthesis

Jialuo Li, Wenhao Chai, Xingyu Fu +2

Current image generation models produce visually compelling but scientifically implausible images, exposing a fundamental gap between visual fidelity and physical realism. In this…

cs.CV2025★ 3 cited

Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps

Nanye Ma, Shangyuan Tong, Haolin Jia +8

Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model…

cs.CV2024

Fast Encoding and Decoding for Implicit Video Representation

Hao Chen, Saining Xie, Ser-Nam Lim +1

Despite the abundant availability and content richness for video data, its high-dimensionality poses challenges for video research. Recent advancements have explored the implicit r…

cs.CV2024

AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark

Wenhao Chai, Enxin Song, Yilun Du +6

Video detailed captioning is a key task which aims to generate comprehensive and coherent textual descriptions of video content, benefiting both video understanding and generation.…