198 citations · 688 across the 92 of their papers we have counts for
13 papers · 1 filter
RoleRMBench & RoleRM: Towards Reward Modeling for Profile-Based Role Play in Dialogue Systems
Hang Ding, Qiming Feng, Dongqi Liu +9
Reward modeling has become a cornerstone of aligning large language models (LLMs) with human preferences. Yet, when extended to subjective and open-ended domains such as role play,…
SwiftVideo: A Unified Framework for Few-Step Video Generation through Trajectory-Distribution Alignment
Yanxiao Sun, Jiafu Wu, Yun Cao +6
Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational ove…
The magnetar model's energy crisis for a prolific repeating fast radio burst source
Jun-Shuo Zhang, Tian-Cong Wang, Pei Wang +61
Fast radio bursts (FRBs) are widely considered to originate from magnetars that power the explosion through releasing magnetic energy. Active repeating FRBs have been seen to produ…
Collaborative Face Experts Fusion in Video Generation: Boosting Identity Consistency Across Large Face Poses
Yuji Wang, Moran Li, Xiaobin Hu +7
Current video generation models struggle with identity preservation under large face poses, primarily facing two challenges: the difficulty in exploring an effective mechanism to i…
Investigating FRB 20240114A with FAST: Morphological Classification and Drifting Rate Measurements in a Burst-Cluster Framework
Long-Xuan Zhang, Shiyan Tian, Junyi Shen +59
This study investigates the morphological classification and drifting rate measurement of the repeating fast radio burst (FRB) source FRB 20240114A using the Five-hundred-meter Ape…
Semantic Frame Interpolation
Yijia Hong, Jiangning Zhang, Ran Yi +4
Generating intermediate video content of varying lengths based on given first and last frames, along with text prompt information, offers significant research and application poten…
HumanVideo-MME: Benchmarking MLLMs for Human-Centric Video Understanding
Yuxuan Cai, Jiangning Zhang, Zhenye Gan +9
Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos. However, their capacity to comprehen…
Identity-Preserving Text-to-Video Generation Guided by Simple yet Effective Spatial-Temporal Decoupled Representations
Yuji Wang, Moran Li, Xiaobin Hu +7
Identity-preserving text-to-video (IPT2V) generation, which aims to create high-fidelity videos with consistent human identity, has become crucial for downstream applications. Howe…
Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Qingdong He, Xueqin Chen, Chaoyi Wang +7
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple and explicit instructions to ex…
UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captions
Zhucun Xue, Jiangning Zhang, Teng Hu +8
The quality of the video dataset (image quality, resolution, and fine-grained caption) greatly influences the performance of the video generation model. The growing demand for vide…
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
Haoxuan Wang, Jinlong Peng, Qingdong He +9
With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can g…
PixelPonder: Dynamic Patch Adaptation for Enhanced Multi-Conditional Text-to-Image Generation
Yanjie Pan, Qingdong He, Zhengkai Jiang +9
Recent advances in diffusion-based text-to-image generation have demonstrated promising results through visual condition control. However, existing ControlNet-like methods struggle…
Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction
Teng Hu, Jiangning Zhang, Ran Yi +5
Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely inv…