923 citations · 1.1k across the 48 of their papers we have counts for
50 papers
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
Mianzhi Liu, Fan Xiao, Zhiliang Yu +5
Retrosynthesis is a cornerstone of drug discovery and organic synthesis. While data-driven deep learning models have shown remarkable progress, they autonomously learn reaction pat…
Qwen-Image-2.0-RL Technical Report
Yixian Xu, Kaiyuan Gao, Yuxiang Chen +25
We present Qwen-Image-2.0-RL, a post-training pipeline that applies reinforcement learning from human feedback (RLHF) and on-policy distillation (OPD) to improve both the visual qu…
SIFT: Self-Imagination Fine-Tuning for Physically Plausible Motion in Video Diffusion Models
Ruoyu Wang, Jialun Liu, Huayang Huang +5
Recent advances in video diffusion models have greatly improved visual fidelity, yet their generated motions often violate physical plausibility. We observe a common kinematic fail…
Qwen-Image-VAE-2.0 Technical Report
Zekai Zhang, Deqing Li, Kuan Cao +27
We present Qwen-Image-VAE-2.0, a suite of high-compression Variational Autoencoders (VAEs) that achieve significant advances in both reconstruction fidelity and diffusability. To a…
Qwen-Image-2.0 Technical Report
Bing Zhao, Chenfei Wu, Deqing Li +72
We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…
Visual Prototype Conditioned Focal Region Generation for UAV-Based Object Detection
Wenhao Li, Zimeng Wu, Yu Wu +2
Unmanned aerial vehicle (UAV) based object detection is a critical but challenging task, when applied in dynamically changing scenarios with limited annotated training data. Layout…
RiskProp: Collision-Anchored Self-Supervised Risk Propagation for Early Accident Anticipation
Yiyang Zou, Tianhao Zhao, Peilun Xiao +9
Accident anticipation aims to predict impending collisions from dashcam videos and trigger early alerts. Existing methods rely on binary supervision with manually annotated "anomal…
Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
Kevin Robbins, Xiaotong Liu, Yu Wu +4
Vision-Language Models like CLIP create aligned embedding spaces for text and images, making it possible for anyone to build a visual classifier by simply naming the classes they w…
RayRoPE: Projective Ray Positional Encoding for Multi-view Attention
Yu Wu, Minsik Jeon, Jen-Hao Rick Chang +2
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-inv…
Text-based Aerial-Ground Person Retrieval
Xinyu Zhou, Yu Wu, Jiayao Ma +3
This work introduces Text-based Aerial-Ground Person Retrieval (TAG-PR), which aims to retrieve person images from heterogeneous aerial and ground views with textual descriptions.…
Towards One-step Causal Video Generation via Adversarial Self-Distillation
Yongqi Yang, Huayang Huang, Xu Peng +5
Recent hybrid video generation models combine autoregressive temporal dynamics with diffusion-based spatial denoising, but their sequential, iterative nature leads to error accumul…
Temporal Score Rescaling for Temperature Sampling in Diffusion and Flow Models
Yanbo Xu, Yu Wu, Sungjae Park +2
We present a mechanism to steer the sampling diversity of denoising diffusion and flow matching models, allowing users to sample from a sharper or broader distribution than the tra…
IS-Diff: Improving Diffusion-Based Inpainting with Better Initial Seed
Yongzhe Lyu, Yu Wu, Yutian Lin +1
Diffusion models have shown promising results in free-form inpainting. Recent studies based on refined diffusion samplers or novel architectural designs led to realistic results an…
Forecasting When to Forecast: Accelerating Diffusion Models with Confidence-Gated Taylor
Xiaoliu Guan, Lielin Jiang, Hanqi Chen +6
Diffusion Transformers (DiTs) have demonstrated remarkable performance in visual generation tasks. However, their low inference speed limits their deployment in low-resource applic…
Rethinking Query-based Transformer for Continual Image Segmentation
Yuchen Zhu, Cheng Shi, Dingyou Wang +5
Class-incremental/Continual image segmentation (CIS) aims to train an image segmenter in stages, where the set of available categories differs at each stage. To leverage the built-…
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement
Qiyuan Dai, Hanzhuo Huang, Yu Wu +1
Generalized Category Discovery (GCD) aims to recognize unlabeled images from known and novel classes by distinguishing novel classes from known ones, while also transferring knowle…
Spotlighting Partially Visible Cinematic Language for Video-to-Audio Generation via Self-distillation
Feizhen Huang, Yu Wu, Yutian Lin +1
Video-to-Audio (V2A) Generation achieves significant progress and plays a crucial role in film and video post-production. However, current methods overlook the cinematic language,…
BNMusic: Blending Environmental Noises into Personalized Music
Chi Zuo, Martin B. Møller, Pablo Martínez-Nuevo +3
While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises w…
Implicit Bias Injection Attacks against Text-to-Image Diffusion Models
Huayang Huang, Xiangye Jin, Jiaxu Miao +1
The proliferation of text-to-image diffusion models (T2I DMs) has led to an increased presence of AI-generated images in daily life. However, biased T2I models can generate content…
Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models
Xiaoliu Guan, Yu Wu, Huayang Huang +3
Diffusion models, known for their tremendous ability to generate high-quality samples, have recently raised concerns due to their data memorization behavior, which poses privacy ri…
The Silent Assistant: NoiseQuery as Implicit Guidance for Goal-Driven Image Generation
Ruoyu Wang, Huayang Huang, Ye Zhu +2
In this work, we introduce NoiseQuery as a novel method for enhanced noise initialization in versatile goal-driven text-to-image (T2I) generation. Specifically, we propose to lever…
Rethinking the Intermediate Features in Adversarial Attacks: Misleading Robotic Models via Adversarial Distillation
Ke Zhao, Huayang Huang, Miao Li +1
Language-conditioned robotic learning has significantly enhanced robot adaptability by enabling a single model to execute diverse tasks in response to verbal commands. Despite thes…
SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generation
Yuhan Pei, Ruoyu Wang, Yongqi Yang +3
Originating from the diffusion phenomenon in physics, which describes the random movement and collisions of particles, diffusion generative models simulate a random walk in the dat…
ROBIN: Robust and Invisible Watermarks for Diffusion Models with Adversarial Optimization
Huayang Huang, Yu Wu, Qian Wang
Watermarking generative content serves as a vital tool for authentication, ownership protection, and mitigation of potential misuse. Existing watermarking methods face the challeng…