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Youfu Li

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6 papers

1 author row

AAAI Conference 2026 Conference Paper

Intention-Aware Diffusion Model for Pedestrian Trajectory Prediction

Yu Liu
Zhijie Liu
Xiao Ren
Youfu Li
He Kong

Predicting pedestrian motion trajectories is critical for the path planning and motion control of autonomous vehicles. Recent diffusion-based models have shown promising results in capturing the inherent stochasticity of pedestrian behavior for trajectory prediction. However, the absence of explicit semantic modelling of pedestrian intent in many diffusion-based methods may result in misinterpreted behaviors and reduced prediction accuracy. To address the above challenges, we propose a diffusion-based pedestrian trajectory prediction framework that incorporates both short-term and long-term motion intentions. Short-term intent is modelled using a residual polar representation, which decouples direction and magnitude to capture fine-grained local motion patterns. Long-term intent is estimated through a learnable, token-based endpoint predictor that generates multiple candidate goals with associated probabilities, enabling multimodal and context-aware intention modelling. Furthermore, we enhance the diffusion process by incorporating adaptive guidance and a residual noise predictor that dynamically refines denoising accuracy. The proposed framework is evaluated on the widely used ETH, UCY, NBA, and SDD benchmarks, demonstrating competitive results against state-of-the-art methods.

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NeurIPS Conference 2025 Conference Paper

EPA: Boosting Event-based Video Frame Interpolation with Perceptually Aligned Learning

Yuhan Liu
LingHui Fu
Zhen Yang
Hao Chen
Youfu Li
Yongjian Deng

Event cameras, with their capacity to provide high temporal resolution information between frames, are increasingly utilized for video frame interpolation (VFI) in challenging scenarios characterized by high-speed motion and significant occlusion. However, prevalent issues of blur and distortion within the keyframes and ground truth data used for training and inference in these demanding conditions are frequently overlooked. This oversight impedes the perceptual realism and multi-scene generalization capabilities of existing event-based VFI (E-VFI) methods when generating interpolated frames. Motivated by the observation that semantic-perceptual discrepancies between degraded and pristine images are considerably smaller than their image-level differences, we introduce EPA. This novel E-VFI framework diverges from approaches reliant on direct image-level supervision by constructing multilevel, degradation-insensitive semantic perceptual supervisory signals to enhance the perceptual realism and multi-scene generalization of the model's predictions. Specifically, EPA operates in two phases: it first employs a DINO-based perceptual extractor, a customized style adapter, and a reconstruction generator to derive multi-layered, degradation-insensitive semantic-perceptual features ($\mathcal{S}$). Second, a novel Bidirectional Event-Guided Alignment (BEGA) module utilizes deformable convolutions to align perceptual features from keyframes to ground truth with inter-frame temporal guidance extracted from event signals. By decoupling the learning process from direct image-level supervision, EPA enhances model robustness against degraded keyframes and unreliable ground truth information. Extensive experiments demonstrate that this approach yields interpolated frames more consistent with human perceptual preferences. *The code will be released upon acceptance. *

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EAAI Journal 2025 Journal Article

Event-based video interpolation via complementary motion information

Yuhan Liu
LingHui Fu
Hao Chen
Zhen Yang
Youfu Li
Yongjian Deng

Video frame interpolation, the task of synthesizing intermediate frames to increase temporal resolution, often struggles with complex scenarios when constrained by the assumption of linear motion The advent of event cameras has led to significant progress in addressing this issue. Event cameras, with microsecond-level temporal resolution, bridge the gap between frames by providing accurate motion cues. However, current event-based video frame interpolation methods often overlook that event data primarily offers high-confidence features at scene edges during multi-modal feature fusion, which may limit the contribution of event signals to optical flow estimation. To address this, we propose a novel end-to-end learning framework that explicitly leverages the complementary characteristics of event signals and frames. Our method synergistically fuses dense contextual information from frames with sparse but precise edge motion from events via a proposed Edge Guided Attention (EGA) module. The EGA employs a coarse-to-fine strategy, where event-based optical flow directly refines the frame-based motion estimation at each level of a pyramidal architecture. Additionally, we introduce an event-based visibility map, co-learned within our event-processing network, to adaptively mitigate occlusions during the warping process. Extensive experiments conducted on a diverse suite of six benchmarks, including four synthetic and two real-world datasets validate the effectiveness of this novel approach. A dedicated discussion of the method’s trade-offs and potential limitations is presented in the Limitations section.

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AAAI Conference 2025 Conference Paper

Know Where You Are From: Event-Based Segmentation via Spatio-Temporal Propagation

Ke Li
Gengyu Lyu
Hao Chen
Bochen Xie
Zhen Yang
Youfu Li
Yongjian Deng

Event cameras have gained attention in segmentation due to their higher temporal resolution and dynamic range compared to traditional cameras. However, they struggle with issues like lack of color perception and triggering only at motion edges, making it hard to distinguish objects with similar contours or segment spatially continuous objects. Our work aims to address these often overlooked issues. Based on the assumption that various objects exhibit different motion patterns, we believe that embedding the historical motion states of objects into segmented scenes can effectively address these challenges. Inspired by this, we propose the ESS framework ``Know Where You Are From" (KWYAF), which incorporates past motion cues through spatio-temporal propagation embedding. This framework features two core components: the Sequential Motion Encoding Module (SME) and the Event-Based Reliable Region Selection Mechanism (ER²SM). SMEs construct prior motion features through spatio-temporal correlation modeling for boosting final segmentation, while ER²SM adapts to identify high-confidence regions, embedding motion more precisely through local window masks and reliable region selection. A large number of experiments have demonstrated the effectiveness of our proposed framework in terms of both quantity and quality.

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EAAI Journal 2025 Journal Article

Pixel-Level Semantics Boosted Fine-Grained Bird Image Classification

Haoxiang Ma
Yongjian Deng
Bochen Xie
Jian Liu
Hai Liu
Youfu Li
Zhen Yang

Fine-grained bird image classification (FBIC) is crucial for endangered bird conservation and biodiversity research. However, existing methods often struggle to capture detailed features and manage the interference caused by complex backgrounds. To address these challenges, we propose a novel Pixel-Level Semantic Boosted Fine-Grained Bird Image Classification (PFIC) framework, which enhances fine-grained bird image classification by incorporating pixel-level semantic information. PFIC consists of two core components: the Grouped Detail Enhancement (GDE) module and the Background–Foreground Enhancement (BFE) strategy. GDE integrates multi-level pixel-level semantic information, derived from a segmentation feature extractor, into classification features via two submodules: grouped aggregation and detail enhancement. This approach enhances the model’s ability to capture fine-grained details. BFE augments training samples by restricting background ranges and applying random shifts to foreground objects, thereby improving the model’s capability to recognize foreground objects in complex environments. Experimental results demonstrate that our method achieves state-of-the-art performance on the CUB-200-2011 and NABirds datasets. Additionally, further experiments on the Stanford Cars dataset validate the framework’s potential for generalization to other fine-grained image classification tasks.

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AAAI Conference 2024 Conference Paper

A Dynamic GCN with Cross-Representation Distillation for Event-Based Learning

Yongjian Deng
Hao Chen
Youfu Li

Recent advances in event-based research prioritize sparsity and temporal precision. Approaches learning sparse point-based representations through graph CNNs (GCN) become more popular. Yet, these graph techniques hold lower performance than their frame-based counterpart due to two issues: (i) Biased graph structures that don't properly incorporate varied attributes (such as semantics, and spatial and temporal signals) for each vertex, resulting in inaccurate graph representations. (ii) A shortage of robust pretrained models. Here we solve the first problem by proposing a new event-based GCN (EDGCN), with a dynamic aggregation module to integrate all attributes of vertices adaptively. To address the second problem, we introduce a novel learning framework called cross-representation distillation (CRD), which leverages the dense representation of events as a cross-representation auxiliary to provide additional supervision and prior knowledge for the event graph. This frame-to-graph distillation allows us to benefit from the large-scale priors provided by CNNs while still retaining the advantages of graph-based models. Extensive experiments show our model and learning framework are effective and generalize well across multiple vision tasks.

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