activity
20242026
most citedA unified multimodal understanding and generation model for cross-disciplinary scientific research

1 citations · 2 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

Diff-Aid: Inference-time Adaptive Interaction Denoising for Rectified Text-to-Image Generation

Binglei Li, Mengping Yang, Zhiyu Tan +2

Recent text-to-image (T2I) diffusion models have achieved remarkable advancement, yet faithfully following complex textual descriptions remains challenging due to insufficient inte…

cs.AI2026★ 1 cited

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…

cs.CV2026

TexTailor: Inference-Time Textual Guidance Tailoring for Multimodal Diffusion Transformers

Binglei Li, Mengping Yang, Zhiyu Tan +2

Recent breakthroughs of transformer-based diffusion models, particularly with Multimodal Diffusion Transformers (MMDiT) driven models like FLUX and Qwen Image, have facilitated thr…

cs.CV2025

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…

cs.CV2025

Omni-Video: Democratizing Unified Video Understanding and Generation

Zhiyu Tan, Hao Yang, Luozheng Qin +3

Notable breakthroughs in unified understanding and generation modeling have led to remarkable advancements in image understanding, reasoning, production and editing, yet current fo…

cs.CV2025

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…

cs.CV2024

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models

Zhiyu Tan, WenXu Qian, Hesen Chen +3

Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remarkable success across various div…

cs.CV2024

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…

cs.CV2024★ 1 cited

An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual Representation

Zhiyu Tan, Mengping Yang, Luozheng Qin +5

One critical prerequisite for faithful text-to-image generation is the accurate understanding of text inputs. Existing methods leverage the text encoder of the CLIP model to repres…