activity
20202026
most citedGiraffeDet: A Heavy-Neck Paradigm for Object Detection

78 citations · 177 across the 32 of their papers we have counts for

collaborators

32 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.LG2026

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…

cs.CV2026

PhyMRI-SR: Toward Physics-Aware MRI Image Super-Resolution

Lihua Wei, Huatong Gao, Jia Gong +4

Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution…

cs.CV2026

Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator

Luozheng Qin, Jia Gong, Qian Qiao +6

Unified multimodal models integrating visual understanding and generation face a fundamental challenge: visual generation incurs substantially higher computational costs than under…

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

RDPO: Real Data Preference Optimization for Physics Consistency Video Generation

Wenxu Qian, Chaoyue Wang, Hou Peng +3

Video generation techniques have achieved remarkable advancements in visual quality, yet faithfully reproducing real-world physics remains elusive. Preference-based model post-trai…

cs.LG2025

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…

math.OC2025

Mirror Descent Linearized Augmented Lagrangian Methods for Nonconvex Constrained Stochastic Zeroth-Order Optimization

Qiankun Shi, Han Yuan, Xiao Wang +1

In this paper, we study nonconvex constrained stochastic zeroth-order optimization problems with exact constraints and stochastic objective evaluations. To solve this class of prob…

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.CV2025

SARA: Structural and Adversarial Representation Alignment for Training-efficient Diffusion Models

Hesen Chen, Junyan Wang, Zhiyu Tan +1

Modern diffusion models encounter a fundamental trade-off between training efficiency and generation quality. While existing representation alignment methods, such as REPA, acceler…

cs.CV2025

Raccoon: Multi-stage Diffusion Training with Coarse-to-Fine Curating Videos

Zhiyu Tan, Junyan Wang, Hao Yang +4

Text-to-video generation has demonstrated promising progress with the advent of diffusion models, yet existing approaches are limited by dataset quality and computational resources…

cs.CV2025

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…

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

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…

cs.CV2024

ReToMe-VA: Recursive Token Merging for Video Diffusion-based Unrestricted Adversarial Attack

Ziyi Gao, Kai Chen, Zhipeng Wei +5

Recent diffusion-based unrestricted attacks generate imperceptible adversarial examples with high transferability compared to previous unrestricted attacks and restricted attacks.…

cs.CV2024★ 1 cited

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…

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…