1 citations · 2 across the 12 of their papers we have counts for
12 papers
DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers
Binglei Li, Mengping Yang, Zhiyu Tan +4
Recent advances in Diffusion Transformers (DiTs) have enabled remarkable progress in visual synthesis, benefiting from their superior scalability. To facilitate DiTs' capability of…
Monkey King Bang: A Unified Scientific Multimodal Foundation Model
Hesen Chen, Xinyu Su, Xiaomeng Yang +11
Scientific discovery is increasingly shifting from isolated disciplines to multi-domain reasoning, and AI for science faces a similar transition. Existing systems are either specia…
Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing
Hao Yang, Zhiyu Tan, Jia Gong +7
We present Omni-Video 2, a scalable and computationally efficient model that connects pretrained multimodal large-language models (MLLMs) with video diffusion models for unified vi…
DiverseDiT: Towards Diverse Representation Learning in Diffusion Transformers
Mengping Yang, Zhiyu Tan, Binglei Li +3
Recent breakthroughs in Diffusion Transformers (DiTs) have revolutionized the field of visual synthesis due to their superior scalability. To facilitate DiTs' capability of capturi…
A unified multimodal understanding and generation model for cross-disciplinary scientific research
Xiaomeng Yang, Zhiyu Tan, Xiaohui Zhong +5
Scientific discovery increasingly relies on integrating heterogeneous, high-dimensional data across disciplines nowadays. While AI models have achieved notable success across vario…
Uni-cot: Towards Unified Chain-of-Thought Reasoning Across Text and Vision
Luozheng Qin, Jia Gong, Yuqing Sun +6
Chain-of-Thought (CoT) reasoning has been widely adopted to enhance Large Language Models (LLMs) by decomposing complex tasks into simpler, sequential subtasks. However, extending…
SIPO: Stabilized and Improved Preference Optimization for Aligning Diffusion Models
Xiaomeng Yang, Mengping Yang, Junyan Wang +3
Preference learning has garnered extensive attention as an effective technique for aligning diffusion models with human preferences in visual generation. However, existing alignmen…
Cockatiel: Ensembling Synthetic and Human Preferenced Training for Detailed Video Caption
Luozheng Qin, Zhiyu Tan, Mengping Yang +2
Video Detailed Captioning (VDC) is a crucial task for vision-language bridging, enabling fine-grained descriptions of complex video content. In this paper, we first comprehensively…
Dual-IPO: Dual-Iterative Preference Optimization for Text-to-Video Generation
Xiaomeng Yang, Mengping Yang, Jia Gong +3
Recent advances in video generation have enabled thrilling experiences in producing realistic videos driven by scalable diffusion transformers. However, they usually fail to produc…
LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
Yibin Wang, Zhiyu Tan, Junyan Wang +3
Recent advances in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human pr…
VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
Zhiyu Tan, Xiaomeng Yang, Luozheng Qin +1
The quality of video-text pairs fundamentally determines the upper bound of text-to-video models. Currently, the datasets used for training these models suffer from significant sho…
EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models
Zhiyu Tan, Xiaomeng Yang, Luozheng Qin +3
The recent advancements in text-to-image generative models have been remarkable. Yet, the field suffers from a lack of evaluation metrics that accurately reflect the performance of…