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Donghao Luo

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13 papers

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AAAI Conference 2026 Conference Paper

SwiftVideo: A Unified Framework for Few-Step Video Generation Through Trajectory-Distribution Alignment

Yanxiao Sun
Jiafu Wu
Yun Cao
Chengming Xu
Yabiao Wang
Weijian Cao
Donghao Luo
Chengjie Wang

Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational overhead. While many distillation methods that are solely based on trajectory-preserving or distribution-matching have been developed to accelerate video generation models, these approaches often suffer from performance breakdown or increased artifacts in few-step settings. To address these limitations, we propose SwiftVideo, a unified and stable distillation framework that combines the advantages of trajectory-preserving and distribution-matching strategies. Our approach introduces continuous-time consistency distillation to ensure precise preservation of ODE trajectories. Subsequently, We propose a dual-perspective alignment encompassing distribution alignment between synthetic and real data along with trajectory alignment across different inference steps. Our method maintains high-quality video generation while substantially reducing the number of inference steps. Quantitative evaluations on the OpenVid-1M benchmark demonstrate that our method significantly outperforms existing approaches in few-step video generation.

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IJCAI Conference 2025 Conference Paper

CrossVTON: Mimicking the Logic Reasoning on Cross-Category Virtual Try-On Guided by Tri-Zone Priors

Donghao Luo
Yujie Liang
Xu Peng
Xiaobin Hu
Boyuan Jiang
Chengming Xu
Taisong Jin
Chengjie Wang

Despite remarkable progress in image-based virtual try-on systems, generating realistic and robust fitting images for cross-category virtual try-on remains a challenging task. The primary difficulty arises from the absence of human-like reasoning, which involves addressing size mismatches between garments and models while recognizing and leveraging the distinct functionalities of various regions within the model images. To address this issue, we draw inspiration from human cognitive processes and disentangle the complex reasoning required for cross-category try-on into a structured framework. This framework systematically decomposes the model image into three distinct regions: try-on, reconstruction, and imagination zones. Each zone plays a specific role in accommodating the garment and facilitating realistic synthesis. To endow the model with robust reasoning capabilities for cross-category scenarios, we propose an iterative data constructor. This constructor encompasses diverse scenarios, including intra-category try-on, any-to-dress transformations (replacing any garment category with a dress), and dress-to-any transformations (replacing a dress with another garment category). Utilizing the generated dataset, we introduce a tri-zone priors generator that intelligently predicts the try-on, reconstruction, and imagination zones by analyzing how the input garment is expected to align with the model image. Guided by these tri-zone priors, our proposed method, CrossVTON, achieves state-of-the-art performance, surpassing existing baselines in both qualitative and quantitative evaluations. Notably, it demonstrates superior capability in handling cross-category virtual try-on, meeting the complex demands of real-world applications.

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NeurIPS Conference 2025 Conference Paper

DecompNet: Enhancing Time Series Forecasting Models with Implicit Decomposition

Donghao Luo
Xue Wang

In this paper, we pioneer the idea of implicit decomposition. And based on this idea, we propose a powerful decomposition-based enhancement framework, namely DecompNet. Our method converts the time series decomposition into an implicit process, where it can give a time series model the decomposition-related knowledge during inference, even though this model does not actually decompose the input time series. Thus, our DecompNet can enable a model to inherit the performance promotion brought by time series decomposition but will not introduce any additional inference costs, successfully enhancing the model performance while enjoying better efficiency. Experimentally, our DecompNet exhibits promising enhancement capability and compelling framework generality. Especially, it can also enhance the performance of the latest and state-of-the-art models, greatly pushing the performance limit of time series forecasting. Through comprehensive comparisons, DecompNet also shows excellent performance and efficiency superiority, making the decomposition-based enhancement framework surpass the well-recognized normalization-based frameworks for the first time. Code is available at this repository: https: //github. com/luodhhh/DecompNet.

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NeurIPS Conference 2024 Conference Paper

DeformableTST: Transformer for Time Series Forecasting without Over-reliance on Patching

Donghao Luo
Xue Wang

With the proposal of patching technique in time series forecasting, Transformerbased models have achieved compelling performance and gained great interest fromthe time series community. But at the same time, we observe a new problem thatthe recent Transformer-based models are overly reliant on patching to achieve idealperformance, which limits their applicability to some forecasting tasks unsuitablefor patching. In this paper, we intent to handle this emerging issue. Through divinginto the relationship between patching and full attention (the core mechanismin Transformer-based models), we further find out the reason behind this issueis that full attention relies overly on the guidance of patching to focus on theimportant time points and learn non-trivial temporal representation. Based on thisfinding, we propose DeformableTST as an effective solution to this emergingissue. Specifically, we propose deformable attention, a sparse attention mechanismthat can better focus on the important time points by itself, to get rid of the need ofpatching. And we also adopt a hierarchical structure to alleviate the efficiency issuecaused by the removal of patching. Experimentally, our DeformableTST achievesthe consistent state-of-the-art performance in a broader range of time series tasks, especially achieving promising performance in forecasting tasks unsuitable forpatching, therefore successfully reducing the reliance on patching and broadeningthe applicability of Transformer-based models. Code is available at this repository: https: //github. com/luodhhh/DeformableTST.

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NeurIPS Conference 2024 Conference Paper

DF40: Toward Next-Generation Deepfake Detection

Zhiyuan Yan
Taiping Yao
Shen Chen
Yandan Zhao
Xinghe Fu
Junwei Zhu
Donghao Luo
Chengjie Wang

We propose a new comprehensive benchmark to revolutionize the current deepfake detection field to the next generation. Predominantly, existing works identify top-notch detection algorithms and models by adhering to the common practice: training detectors on one specific dataset ( e. g. , FF++) and testing them on other prevalent deepfake datasets. This protocol is often regarded as a "golden compass" for navigating SoTA detectors. But can these stand-out "winners" be truly applied to tackle the myriad of realistic and diverse deepfakes lurking in the real world? If not, what underlying factors contribute to this gap? In this work, we found the dataset (both train and test) can be the "primary culprit" due to the following: (1) forgery diversity: Deepfake techniques are commonly referred to as both face forgery (face-swapping and face-reenactment) and entire image synthesis (AIGC, especially face). Most existing datasets only contain partial types of them, with limited forgery methods implemented ( e. g. , 2 swapping and 2 reenactment methods in FF++); (2) forgery realism: The dominated training dataset, FF++, contains out-of-date forgery techniques from the past four years. "Honing skills" on these forgeries makes it difficult to guarantee effective detection generalization toward nowadays' SoTA deepfakes; (3) evaluation protocol: Most detection works perform evaluations on one type, e. g. , face-swapping types only, which hinders the development of universal deepfake detectors. To address this dilemma, we construct a highly diverse and large-scale deepfake detection dataset called DF40, which comprises 40 distinct deepfake techniques (10 times larger than FF++). We then conduct comprehensive evaluations using 4 standard evaluation protocols and 8 representative detection methods, resulting in over 2, 000 evaluations. Through these evaluations, we provide an extensive analysis from various perspectives, leading to 7 new insightful findings contributing to the field. We also open up 4 valuable yet previously underexplored research questions to inspire future works. We release our dataset, code, and pre-trained weights at https: //github. com/YZY-stack/DF40.

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EAAI Journal 2024 Journal Article

Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

Haiming Yao
Wei Luo
Weihang Zhang
Xiaotian Zhang
Zhenfeng Qiang
Donghao Luo

The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.

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AAAI Conference 2023 Conference Paper

High-Resolution GAN Inversion for Degraded Images in Large Diverse Datasets

Yanbo Wang
Chuming Lin
Donghao Luo
Ying Tai
Zhizhong Zhang
Yuan Xie

The last decades are marked by massive and diverse image data, which shows increasingly high resolution and quality. However, some images we obtained may be corrupted, affecting the perception and the application of downstream tasks. A generic method for generating a high-quality image from the degraded one is in demand. In this paper, we present a novel GAN inversion framework that utilizes the powerful generative ability of StyleGAN-XL for this problem. To ease the inversion challenge with StyleGAN-XL, Clustering \& Regularize Inversion (CRI) is proposed. Specifically, the latent space is firstly divided into finer-grained sub-spaces by clustering. Instead of initializing the inversion with the average latent vector, we approximate a centroid latent vector from the clusters, which generates an image close to the input image. Then, an offset with a regularization term is introduced to keep the inverted latent vector within a certain range. We validate our CRI scheme on multiple restoration tasks (i.e., inpainting, colorization, and super-resolution) of complex natural images, and show preferable quantitative and qualitative results. We further demonstrate our technique is robust in terms of data and different GAN models. To our best knowledge, we are the first to adopt StyleGAN-XL for generating high-quality natural images from diverse degraded inputs. Code is available at https://github.com/Booooooooooo/CRI.

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AAAI Conference 2023 Conference Paper

High-Resolution Iterative Feedback Network for Camouflaged Object Detection

Xiaobin Hu
Shuo Wang
Xuebin Qin
Hang Dai
Wenqi Ren
Donghao Luo
Ying Tai
Ling Shao

Spotting camouflaged objects that are visually assimilated into the background is tricky for both object detection algorithms and humans who are usually confused or cheated by the perfectly intrinsic similarities between the foreground objects and the background surroundings. To tackle this challenge, we aim to extract the high-resolution texture details to avoid the detail degradation that causes blurred vision in edges and boundaries. We introduce a novel HitNet to refine the low-resolution representations by high-resolution features in an iterative feedback manner, essentially a global loop-based connection among the multi-scale resolutions. To design better feedback feature ﬂow and avoid the feature corruption caused by recurrent path, an iterative feedback strategy is proposed to impose more constraints on each feedback connection. Extensive experiments on four challenging datasets demonstrate that our HitNet breaks the performance bottleneck and achieves significant improvements compared with 29 state-of-the-art methods. In addition, to address the data scarcity in camouflaged scenarios, we provide an application example to convert the salient objects to camouflaged objects, thereby generating more camouflaged training samples from the diverse salient object datasets. Code will be made publicly available.

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AAAI Conference 2021 Conference Paper

Learning Comprehensive Motion Representation for Action Recognition

Mingyu Wu
Boyuan Jiang
Donghao Luo
Junchi Yan
Yabiao Wang
Ying Tai
Chengjie Wang
Jilin Li

For action recognition learning, 2D CNN-based methods are efficient but may yield redundant features due to applying the same 2D convolution kernel to each frame. Recent efforts attempt to capture motion information by establishing interframe connections while still suffering the limited temporal receptive field or high latency. Moreover, the feature enhancement is often only performed by channel or space dimension in action recognition. To address these issues, we first devise a Channel-wise Motion Enhancement (CME) module to adaptively emphasize the channels related to dynamic information with a channel-wise gate vector. The channel gates generated by CME incorporate the information from all the other frames in the video. We further propose a Spatial-wise Motion Enhancement (SME) module to focus on the regions with the critical target in motion, according to the point-to-point similarity between adjacent feature maps. The intuition is that the change of background is typically slower than the motion area. Both CME and SME have clear physical meaning in capturing action clues. By integrating the two modules into the off-the-shelf 2D network, we finally obtain a Comprehensive Motion Representation (CMR) learning method for action recognition, which achieves competitive performance on Something-Something V1 & V2 and Kinetics-400. On the temporal reasoning datasets Something-Something V1 and V2, our method outperforms the current state-of-the-art by 2. 3% and 1. 9% when using 16 frames as input, respectively.

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NeurIPS Conference 2021 Conference Paper

Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution

Guangpin Tao
Xiaozhong Ji
Wenzhuo Wang
Shuo Chen
Chuming Lin
Yun Cao
Tong Lu
Donghao Luo

Deep-learning based Super-Resolution (SR) methods have exhibited promising performance under non-blind setting where blur kernel is known; however, blur kernels of Low-Resolution (LR) images in different practical applications are usually unknown. It may lead to a significant performance drop when degradation process of training images deviates from that of real images. In this paper, we propose a novel blind SR framework to super-resolve LR images degraded by arbitrary blur kernel with accurate kernel estimation in frequency domain. To our best knowledge, this is the first deep learning method which conducts blur kernel estimation in frequency domain. Specifically, we first demonstrate that feature representation in frequency domain is more conducive for blur kernel reconstruction than in spatial domain. Next, we present a Spectrum-to-Kernel (S$2$K) network to estimate general blur kernels in diverse forms. We use a conditional GAN (CGAN) combined with SR-oriented optimization target to learn the end-to-end translation from degraded images' spectra to unknown kernels. Extensive experiments on both synthetic and real-world images demonstrate that our proposed method sufficiently reduces blur kernel estimation error, thus enables the off-the-shelf non-blind SR methods to work under blind setting effectively, and achieves superior performance over state-of-the-art blind SR methods, averagely by 1. 39dB, 0. 48dB (Gaussian kernels) and 6. 15dB, 4. 57dB (motion kernels) for scales $2\times$ and $4\times$ respectively.

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AAAI Conference 2020 Conference Paper

Fast Learning of Temporal Action Proposal via Dense Boundary Generator

Chuming Lin
Jian Li
Yabiao Wang
Ying Tai
Donghao Luo
Zhipeng Cui
Chengjie Wang
Jilin Li

Generating temporal action proposals remains a very challenging problem, where the main issue lies in predicting precise temporal proposal boundaries and reliable action conﬁdence in long and untrimmed real-world videos. In this paper, we propose an efﬁcient and uniﬁed framework to generate temporal action proposals named Dense Boundary Generator (DBG), which draws inspiration from boundary-sensitive methods and implements boundary classiﬁcation and action completeness regression for densely distributed proposals. In particular, the DBG consists of two modules: Temporal boundary classiﬁcation (TBC) and Action-aware completeness regression (ACR). The TBC aims to provide two temporal boundary conﬁdence maps by low-level two-stream features, while the ACR is designed to generate an action completeness score map by high-level action-aware features. Moreover, we introduce a dual stream BaseNet (DSB) to encode RGB and optical ﬂow information, which helps to capture discriminative boundary and actionness features. Extensive experiments on popular benchmarks ActivityNet-1. 3 and THUMOS14 demonstrate the superiority of DBG over the state-of-the-art proposal generator (e. g. , MGG and BMN).

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AAAI Conference 2020 Conference Paper

TEINet: Towards an Efficient Architecture for Video Recognition

Zhaoyang Liu
Donghao Luo
Yabiao Wang
Limin Wang
Ying Tai
Chengjie Wang
Jilin Li
Feiyue Huang

Efﬁciency is an important issue in designing video architectures for action recognition. 3D CNNs have witnessed remarkable progress in action recognition from videos. However, compared with their 2D counterparts, 3D convolutions often introduce a large amount of parameters and cause high computational cost. To relieve this problem, we propose an efﬁcient temporal module, termed as Temporal Enhancementand-Interaction (TEI Module), which could be plugged into the existing 2D CNNs (denoted by TEINet). The TEI module presents a different paradigm to learn temporal features by decoupling the modeling of channel correlation and temporal interaction. First, it contains a Motion Enhanced Module (MEM) which is to enhance the motion-related features while suppress irrelevant information (e. g. , background). Then, it introduces a Temporal Interaction Module (TIM) which supplements the temporal contextual information in a channel-wise manner. This two-stage modeling scheme is not only able to capture temporal structure ﬂexibly and effectively, but also efﬁcient for model inference. We conduct extensive experiments to verify the effectiveness of TEINet on several benchmarks (e. g. , Something-Something V1&V2, Kinetics, UCF101 and HMDB51). Our proposed TEINet can achieve a good recognition accuracy on these datasets but still preserve a high efﬁciency.

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IJCAI Conference 2017 Conference Paper

Image Matching via Loopy RNN

Donghao Luo
Bingbing Ni
Yichao Yan
Xiaokang Yang

Most existing matching algorithms are one-off algorithms, i. e. , they usually measure the distance between the two image feature representation vectors for only one time. In contrast, human's vision system achieves this task, i. e. , image matching, by recursively looking at specific/related parts of both images and then making the final judgement. Towards this end, we propose a novel loopy recurrent neural network (Loopy RNN), which is capable of aggregating relationship information of two input images in a progressive/iterative manner and outputting the consolidated matching score in the final iteration. A Loopy RNN features two uniqueness. First, built on conventional long short-term memory (LSTM) nodes, it links the output gate of the tail node to the input gate of the head node, thus it brings up symmetry property required for matching. Second, a monotonous loss designed for the proposed network guarantees increasing confidence during the recursive matching process. Extensive experiments on several image matching benchmarks demonstrate the great potential of the proposed method.

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