Author name cluster

Zhenyu Yang

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11 papers

2 author rows

AAAI Conference 2026 Conference Paper

Beyond Quadratic: Linear-Time Change Detection with RWKV

Zhenyu Yang
Gensheng Pei
Tao Chen
Xia Yuan
Haofeng Zhang
Xiangbo Shu
Yazhou Yao

Existing paradigms for remote sensing change detection are caught in a trade-off: CNNs excel at efficiency but lack global context, while Transformers capture long-range dependencies at a prohibitive computational cost. This paper introduces ChangeRWKV, a new architecture that reconciles this conflict. By building upon the Receptance Weighted Key Value (RWKV) framework, our ChangeRWKV uniquely combines the parallelizable training of Transformers with the linear-time inference of RNNs. Our approach core features two key innovations: a hierarchical RWKV encoder that builds multi-resolution feature representation, and a novel Spatial-Temporal Fusion Module (STFM) engineered to resolve spatial misalignments across scales while distilling fine-grained temporal discrepancies. ChangeRWKV not only achieves state-of-the-art performance on the LEVIR-CD benchmark, with an 85.46% IoU and 92.16% F1 score, but does so while drastically reducing parameters and FLOPs compared to previous leading methods. This work demonstrates a new, efficient, and powerful paradigm for operational-scale change detection.

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ICLR Conference 2025 Conference Paper

FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality

Zhengyao Lv
Chenyang Si
Junhao Song
Zhenyu Yang
Yu Qiao 0001
Ziwei Liu 0002
Kwan-Yee K. Wong

In this paper, we present \textbf{\textit{FasterCache}}, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. By analyzing existing cache-based methods, we observe that \textit{directly reusing adjacent-step features degrades video quality due to the loss of subtle variations}. We further perform a pioneering investigation of the acceleration potential of classifier-free guidance (CFG) and reveal significant redundancy between conditional and unconditional features within the same timestep. Capitalizing on these observations, we introduce FasterCache to substantially accelerate diffusion-based video generation. Our key contributions include a dynamic feature reuse strategy that preserves both feature distinction and temporal continuity, and CFG-Cache which optimizes the reuse of conditional and unconditional outputs to further enhance inference speed without compromising video quality. We empirically evaluate FasterCache on recent video diffusion models. Experimental results show that FasterCache can significantly accelerate video generation (\eg 1.67$\times$ speedup on Vchitect-2.0) while keeping video quality comparable to the baseline, and consistently outperform existing methods in both inference speed and video quality. \textit{Our code will be made public upon publication.}

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AAAI Conference 2025 Conference Paper

GlyphDraw2: Automatic Generation of Complex Glyph Posters with Diffusion Models and Large Language Models

Jian Ma
Yonglin Deng
Chen Chen
Nanyang Du
Haonan Lu
Zhenyu Yang

Posters serve an essential function in marketing and advertising by improving visual communication and brand visibility, thus significantly contributing to industrial design. With the latest developments in controllable T2I diffusion models, research interest has surged in text rendering within synthesized images. Although text rendering accuracy has seen advancements, automatic poster generation remains a relatively untapped area. This paper presents an automatic poster generation framework featuring text rendering capabilities through the use of LLMs. Our framework employs a triple-cross attention mechanism based on alignment learning to achieve precise text placement within detailed contextual backgrounds. Moreover, it supports adjustable fonts, varying image resolutions, and poster rendering with textual prompts in both English and Chinese. Additionally, we present a comprehensive bilingual image-text dataset, GlyphDraw-3M, comprising 3 million image-text pairs, each with OCR annotations and resolutions exceeding 1024. Our method utilizes the SDXL architecture, and extensive experiments confirm its ability to generate posters with intricate and context-rich backgrounds.

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NeurIPS Conference 2025 Conference Paper

LiveStar: Live Streaming Assistant for Real-World Online Video Understanding

Zhenyu Yang
Kairui Zhang
Yuhang Hu
Bing Wang
Shengsheng Qian
Bin Wen
Fan Yang
Tingting Gao

Despite significant progress in Video Large Language Models (Video-LLMs) for offline video understanding, existing online Video-LLMs typically struggle to simultaneously process continuous frame-by-frame inputs and determine optimal response timing, often compromising real-time responsiveness and narrative coherence. To address these limitations, we introduce LiveStar, a pioneering live streaming assistant that achieves always-on proactive responses through adaptive streaming decoding. Specifically, LiveStar incorporates: (1) a training strategy enabling incremental video-language alignment for variable-length video streams, preserving temporal consistency across dynamically evolving frame sequences; (2) a response-silence decoding framework that determines optimal proactive response timing via a single forward pass verification; (3) memory-aware acceleration via peak-end memory compression for online inference on 10+ minute videos, combined with streaming key-value cache to achieve 1. 53× faster inference. We also construct an OmniStar dataset, a comprehensive dataset for training and benchmarking that encompasses 15 diverse real-world scenarios and 5 evaluation tasks for online video understanding. Extensive experiments across three benchmarks demonstrate LiveStar's state-of-the-art performance, achieving an average 19. 5\% improvement in semantic correctness with 18. 1\% reduced timing difference compared to existing online Video-LLMs, while improving FPS by 12. 0\% across all five OmniStar tasks. Our model and dataset can be accessed at https: //github. com/yzy-bupt/LiveStar.

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IJCAI Conference 2025 Conference Paper

MsRAG: Knowledge Augumented Image Captioning with Object-level Multi-source RAG

Yuming Qiao
Yuechen Wang
Dan Meng
Haonan Lu
Zhenyu Yang
Xudong Zhang

Language-Visual Large Models (LVLMs) have made significant strides in enhancing visual understanding capabilities. However, these models often struggle with knowledge-based visual tasks due to constrains in their pre-training data scope and timeliness. Existing Retrieval-Augmented Generation (RAG) methods can effectively solve the problem but primarily rely on user queries, limiting their applicability in scenarios without explicit language input. To overcome these challenges, we introduce MsRAG, a knowledge-augmented captioning framework designed to effectively retrieve and utilize external real-world knowledge, particularly in the absence of user queries, and perform dense captioning for subjects. MsRAG comprises three key components: (1) Parallel Visual Search Module. It retrieves fine-grained object-level knowledge using both online visual search engines and offline domain-knowledge databases, enhancing the robustness and richness of retrieved information. (2) Prompt Templates Pool. The prompt pool dynamically assigns appropriate prompts based on retrieved information, optimizing LVLMs' ability to leverage relevant data under complex RAG conditions. (3) Visual-RAG Alignment Module, which employs a novel visual prompting method to bridge the modality gap between textual RAG content and corresponding visual objects, enabling precise alignment of visual elements with their text-format RAG content. To validate the effectiveness of MsRAG, we conducted a series of qualitative and quantitative experiments. The evaluation results demonstrate the superiority of MsRAG over other methods.

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AAAI Conference 2023 Conference Paper

Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework

Jian Guan
Zhenyu Yang
Rongsheng Zhang
Zhipeng Hu
Minlie Huang

Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories and news. We conjecture that such issues result from representing entities as static embeddings of superficial words, while neglecting to model their ever-changing states, i.e., the information they carry, as the text unfolds. Therefore, we extend the Transformer model to dynamically conduct entity state updates and sentence realization for narrative generation. We propose a contrastive framework to learn the state representations in a discrete space, and insert additional attention layers into the decoder to better exploit these states. Experiments on two narrative datasets show that our model can generate more coherent and diverse narratives than strong baselines with the guidance of meaningful entity states.

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IJCAI Conference 2022 Conference Paper

Curriculum-Based Self-Training Makes Better Few-Shot Learners for Data-to-Text Generation

Pei Ke
Haozhe Ji
Zhenyu Yang
Yi Huang
Junlan Feng
Xiaoyan Zhu
Minlie Huang

Despite the success of text-to-text pre-trained models in various natural language generation (NLG) tasks, the generation performance is largely restricted by the number of labeled data in downstream tasks, particularly in data-to-text generation tasks. Existing works mostly utilize abundant unlabeled structured data to conduct unsupervised pre-training for task adaption, which fail to model the complex relationship between source structured data and target texts. Thus, we introduce self-training as a better few-shot learner than task-adaptive pre-training, which explicitly captures this relationship via pseudo-labeled data generated by the pre-trained model. To alleviate the side-effect of low-quality pseudo-labeled data during self-training, we propose a novel method called Curriculum-Based Self-Training (CBST) to effectively leverage unlabeled data in a rearranged order determined by the difficulty of text generation. Experimental results show that our method can outperform fine-tuning and task-adaptive pre-training methods, and achieve state-of-the-art performance in the few-shot setting of data-to-text generation.

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NeurIPS Conference 2022 Conference Paper

Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection

Ge Zhang
Zhenyu Yang
Jia Wu
Jian Yang
Shan Xue
Hao Peng
Jianlin Su
Chuan Zhou

Graph-level anomaly detection aims to distinguish anomalous graphs in a graph dataset from normal graphs. Anomalous graphs represent a very few but essential patterns in the real world. The anomalous property of a graph may be referable to its anomalous attributes of particular nodes and anomalous substructures that refer to a subset of nodes and edges in the graph. In addition, due to the imbalance nature of anomaly problem, anomalous information will be diluted by normal graphs with overwhelming quantities. Various anomaly notions in the attributes and/or substructures and the imbalance nature together make detecting anomalous graphs a non-trivial task. In this paper, we propose a graph neural network for graph-level anomaly detection, namely iGAD. Specifically, an anomalous graph attribute-aware graph convolution and an anomalous graph substructure-aware deep Random Walk Kernel (deep RWK) are welded into a graph neural network to achieve the dual-discriminative ability on anomalous attributes and substructures. Deep RWK in iGAD makes up for the deficiency of graph convolution in distinguishing structural information caused by the simple neighborhood aggregation mechanism. Further, we propose a Point Mutual Information (PMI)-based loss function to target the problems caused by imbalance distributions. PMI-based loss function enables iGAD to capture essential correlation between input graphs and their anomalous/normal properties. We evaluate iGAD on four real-world graph datasets. Extensive experiments demonstrate the superiority of iGAD on the graph-level anomaly detection task.

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ICRA Conference 2019 Conference Paper

Learning to Capture a Film-Look Video with a Camera Drone

Chong Huang 0005
Zhenyu Yang
Yan Kong
Peng Chen 0008
Xin Yang 0008
Kwang-Ting (Tim) Cheng

The development of intelligent drones has simplified aerial filming and provided smarter assistant tools for users to capture a film-look footage. Existing methods of autonomous aerial filming either specify predefined camera movements for a drone to capture a footage, or employ heuristic approaches for camera motion planning. However, both predefined movements and heuristically planned motions are hardly able to provide cinematic footages for various dynamic scenarios. In this paper, we propose a data-driven learning-based approach, which can imitate a professional cameraman's intention for capturing a film-look aerial footage of a single subject in real-time. We model the decision-making process of the cameraman with two steps: 1) we train a network to predict the future image composition and camera position, and 2) our system then generates control commands to achieve the desired shot framing. At the system level, we deploy our algorithm on the limited resources of a drone and demonstrate the feasibility of running automatic filming onboard in real-time. Our experiments show how our data-driven planning approach achieves film-look footages and successfully mimics the work of a professional cameraman.

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ICRA Conference 2018 Conference Paper

ACT: An Autonomous Drone Cinematography System for Action Scenes

Chong Huang 0005
Fei Gao 0011
Jie Pan 0004
Zhenyu Yang
Weihao Qiu
Peng Chen 0008
Xin Yang 0008
Shaojie Shen

Drones are enabling new forms of cinematography. Aerial filming via drones in action scenes is difficult because it requires users to understand the dynamic scenarios and operate the drone and camera simultaneously. Existing systems allow the user to manually specify the shots and guide the drone to capture footage, while none of them employ aesthetic objectives to automate aerial filming in action scenes. Meanwhile, these drone cinematography systems depend on the external motion capture systems to perceive the human action, which is limited to the indoor environment. In this paper, we propose an Autonomous CinemaTography system “ACT” on the drone platform to address the above the challenges. To our knowledge, this is the first drone camera system which can autonomously capture cinematic shots of action scenes based on limb movements in both indoor and outdoor environments. Our system includes the following novelties. First, we propose an efficient method to extract 3D skeleton points via a stereo camera. Second, we design a real-time dynamical camera planning strategy that fulfills the aesthetic objectives for filming and respects the physical limits of a drone. At the system level, we integrate cameras and GPUs into the limited space of a drone and demonstrate the feasibility of running the entire cinematography system onboard in real-time. Experimental results in both simulation and real-world scenarios demonstrate that our cinematography system “ACT” can capture more expressive video footage of human action than that of a state-of-the-art drone camera system.

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IROS Conference 2018 Conference Paper

Through-the-Lens Drone Filming

Chong Huang 0005
Zhenyu Yang
Yan Kong
Peng Chen 0008
Xin Yang 0008
Kwang-Ting (Tim) Cheng

Aerial filming in action scenes using a drone is difficult for inexperienced flyers because manipulating a remote controller and meeting the desired image composition are two independent, while concurrent, tasks. Existing systems attempt to utilize wearable GPS-based or infrared-based sensors to track the human movement and to assist in capturing footage. However, these sensors work only in either indoor (infrared-based) or outdoor environments (GPS-based), but not both. In this paper, we introduce a novel drone filming system which integrates monocular 3D human pose estimation and localization into a drone platform to remove the constraints imposed by wearable-sensor-based solutions. Meanwhile, given the estimated position, we propose a novel drone control system, called “through-the-lens drone filming”, to allow a cameraman to conveniently control the drone by manipulating a 3D model in the preview, which closes the gap between the flight control and the viewpoint design. Our system includes two key enabling techniques: 1) subject localization based on visual-inertial fusion, and 2) through-the-lens camera planning. This is the first drone camera system which allows users to capture human actions by manipulating the camera in a virtual environment. From the drone hardware, we integrate a gimbal camera and two GPUs into the limited space of a drone and demonstrate the feasibility of running the entire system onboard with insignificant delays, which are sufficient for filming in our real-time application. Experimental results, in both simulation and real-world scenarios, demonstrate that our techniques can greatly ease camera control and capture better videos.

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