8 citations · 15 across the 6 of their papers we have counts for
6 papers
DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation
Wangbo Zhao, Yizeng Han, Jiasheng Tang +6
Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior performance but suffers from substantial computational costs. Our investig…
Rethinking Human Evaluation Protocol for Text-to-Video Models: Enhancing Reliability,Reproducibility, and Practicality
Tianle Zhang, Langtian Ma, Yuchen Yan +9
Recent text-to-video (T2V) technology advancements, as demonstrated by models such as Gen2, Pika, and Sora, have significantly broadened its applicability and popularity. Despite t…
Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond
Zheng Zhu, Xiaofeng Wang, Wangbo Zhao +15
General world models represent a crucial pathway toward achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications ranging from virtual en…
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation
Wangbo Zhao, Jiasheng Tang, Yizeng Han +5
Existing parameter-efficient fine-tuning (PEFT) methods have achieved significant success on vision transformers (ViTs) adaptation by improving parameter efficiency. However, the e…
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Yuchen Zhang, Tianle Zhang, Kai Wang +5
Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing a compact counterpart without sacrificing the performance of Graph Neural Networks (GNNs)…
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient Matching
Tianle Zhang, Yuchen Zhang, Kun Wang +7
Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have raised growing concerns. As one of the most promising…