Jin Wang | ViLab

Efficient event-based semantic segmentation via exploiting frame-event fusion: A hybrid neural network approach

Fri, 11 Apr 2025 00:00:00 +0000

本文提出一种高效的混合神经网络框架，用于事件相机语义分割。该框架包含处理事件流的脉冲神经网络（SNN）分支和处理帧图像的人工神经网络（ANN）分支，并设计了自适应时间加权（ATW）注入器、事件驱动稀疏（EDS）注入器和通道选择融合（CSF）模块，以充分融合帧与事件的互补时空信息。在DDD17-Seg、DSEC-Semantic和M3ED-Semantic数据集上取得了最先进精度，并在DSEC-Semantic上降低63%能耗。

Event-enhanced blurry video super-resolution

Fri, 11 Apr 2025 00:00:00 +0000

In this paper, we tackle the task of blurry video super-resolution (BVSR), aiming to generate high-resolution (HR) videos from low-resolution (LR) and blurry inputs. Current BVSR methods often fail to restore sharp details at high resolutions, resulting in noticeable artifacts and jitter due to insufficient motion information for deconvolution and the lack of high-frequency details in LR frames. To address these challenges, we introduce event signals into BVSR and propose a novel event-enhanced network, Ev-DeblurVSR. To effectively fuse information from frames and events for feature deblurring, we introduce a reciprocal feature deblurring module that leverages motion information from intra-frame events to deblur frame features while reciprocally using global scene context from the frames to enhance event features. Furthermore, to enhance temporal consistency, we propose a hybrid deformable alignment module that fully exploits the complementary motion information from inter-frame events and optical flow to improve motion estimation in the deformable alignment process. Extensive evaluations demonstrate that Ev-DeblurVSR establishes a new state-of-the-art performance on both synthetic and real-world datasets. Notably, on real data, our method is 2.59 dB more accurate and 7.28× faster than the recent best BVSR baseline FMA-Net.

Event-based head pose estimation: Benchmark and method

Sun, 29 Sep 2024 00:00:00 +0000

本文针对传统RGB方法在剧烈运动和极端光照下头部姿态估计困难的问题，引入事件相机的高时间分辨率与高动态范围优势。作者构建了两个大规模事件头部姿态数据集，包含282个序列，覆盖不同分辨率与场景；并提出事件头部姿态估计网络EV-HPE，设计了事件时空融合模块和事件运动感知注意力模块，有效结合事件流时空信息，提升姿态估计精度与鲁棒性。

Event-assisted low-light video object segmentation

Sun, 16 Jun 2024 00:00:00 +0000

本文针对低光照条件下视频目标分割（VOS）性能严重下降的问题，提出一种利用事件相机数据辅助分割的新框架。该方法包含两个关键模块：自适应跨模态融合（ACMF）模块，用于提取并融合图像与事件模态特征以抑制噪声干扰；事件引导记忆匹配（EGMM）模块，用于修正低光下查询帧与记忆帧之间的相似度计算误差。实验表明，该方法在合成和真实低光数据集上均能显著提升分割精度，生成更准确的目标掩码。