FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness

• URL: http://arxiv.org/abs/2606.06856v1
• Date: Fri, 05 Jun 2026 03:00:36 GMT
• Title: FS-DVS: A Frequency-Selective Dynamic Visual Sensing Paradigm for Enhancing Information Completeness
• Abstract summary: Dynamic vision sensors (DVS) offer exceptional temporal resolution and dynamic range by asynchronously reporting pixel-level intensity changes.<n>We propose FS-DVS (Frequency-Selective Dynamic Vision Sensor), a novel paradigm that integrates a learnable spatial filter.<n>By developing a differentiable event simulation framework, the spatial filter can be optimized end-to-end with downstream tasks.
• Score: 30.541053618924277
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Dynamic vision sensors (DVS) offer exceptional temporal resolution and dynamic range by asynchronously reporting pixel-level intensity changes. However, conventional DVS rely on a per-pixel independent triggering mechanism, ignoring the spatial integration performed by biological retinal ganglion cells (RGCs). Consequently, they lack the contrast sensitivity function (CSF) and its inherent sensitivity to mid-spatial frequencies, which inevitably leads to information incompleteness due to sub-threshold signal loss. To bridge this gap, we propose FS-DVS (Frequency-Selective Dynamic Vision Sensor), a novel paradigm that integrates a learnable spatial filter strictly preceding the event triggering process to mimic the RGC aggregation mechanism. By developing a differentiable event simulation framework, the spatial filter can be optimized end-to-end with downstream tasks. Our study reveals that starting from a delta function, the learned spatial filters spontaneously evolve into center-surround patterns that emphasize mid-frequency components, consistently aligning with human CSF. Beyond achieving substantial performance gains in object detection and action recognition, the consistent convergence to human-like CSF characteristics across different tasks underscores the universality of this mid-frequency selective mechanism. Compared to naively increasing sensor sensitivity or relying on post-processing, our paradigm achieves selective information enhancement with high noise resilience, providing a robust, biologically plausible blueprint for next-generation neuromorphic sensors.

Related papers

• Sen-Cap: Sensor-Flexible and Noise-Resilient Human Motion Capture via LiDAR-Camera Integration [50.18488308004802]
  Sen-Cap is a 3D motion capture framework that integrates multi-modal data from LiDAR and camera.<n>Sen-Cap achieves state-of-the-art performance on major metrics on Human-M3 and FreeMotion.<n>This combination of flexibility and robustness opens new opportunities for motion capture in real-world scenarios.
  arXiv  Detail & Related papers  (2026-08-03T14:16:18Z)
• TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning [9.697220089288564]
  We propose a scan-aware PEIVTL framework that explicitly models fine-temporal variations to enable reliable ultrasound video understanding.<n>Experiments on in-house ultrasound trauma datasets demonstrate that TRUST outperforms state-of-the-art methods by 9.63% with superior computational efficiency.
  arXiv  Detail & Related papers  (2026-06-26T07:06:34Z)
• Feature Visualization Recovers Known Cortical Selectivity from TRIBE v2 [0.19336815376402716]
  Brain encoder models predict cortical fMRI responses from the internal activations of pretrained vision and language networks.<n>We propose feature visualization as a complementary interpretability technique.<n>The probe is simple, differentiable, and applicable to any brain encoder with a differentiable backbone.
  arXiv  Detail & Related papers  (2026-05-13T00:55:45Z)
• Triple Spectral Fusion for Sensor-based Human Activity Recognition [86.21994396715074]
  This paper proposes a novel triple spectral fusion framework tailored for sensor-based human activity recognition (HAR)<n>We develop an adaptive complementary filtering technique for noise suppression and organize each IMU's sensors into posture and motion modality nodes.<n>An adaptive wavelet frequency selection approach is implemented to suppress context redundancy and shorten the length of features.
  arXiv  Detail & Related papers  (2026-05-04T15:42:58Z)
• Robust Cross-Domain WiFi Fall Detection via Physics-Driven Attention-Enhanced Transformers [64.35403680946182]
  Device-free fall detection utilizing WiFi Channel State Information (CSI) has emerged as a promising solution for elderly health monitoring.<n>Existing deep learning approaches suffer from severe performance degradation when deployed in unseen environments.<n>We propose a robust, domain-general framework featuring a novel Attention-Enhanced CNN-Transformer hybrid architecture.
  arXiv  Detail & Related papers  (2026-04-23T03:28:12Z)
• Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection [67.84730634802204]
  Change detection in remote sensing imagery plays a vital role in various engineering applications, such as natural disaster monitoring, urban expansion tracking, and infrastructure management.<n>Most existing methods still rely on spatial-domain modeling, where the limited diversity of feature representations hinders the detection of subtle change regions.<n>We observe that frequency-domain feature modeling particularly in the wavelet domain amplify fine-grained differences in frequency components, enhancing the perception of edge changes that are challenging to capture in the spatial domain.
  arXiv  Detail & Related papers  (2025-08-07T11:14:16Z)
• FreqDGT: Frequency-Adaptive Dynamic Graph Networks with Transformer for Cross-subject EEG Emotion Recognition [1.9198890060313585]
  Cross-subject generalization is a challenge due to individual variability, cognitive traits, and emotional responses.<n>We propose FreqDGT, a frequency-adaptive dynamic graph transformer that addresses these limitations through an integrated framework.<n>FreqDGT significantly improves cross-subject emotion recognition accuracy, confirming the effectiveness of integrating frequency-adaptive, spatial-dynamic, and temporal-hierarchical modeling.
  arXiv  Detail & Related papers  (2025-06-28T08:18:05Z)
• Efficient and Robust Multidimensional Attention in Remote Physiological Sensing through Target Signal Constrained Factorization [7.947387272047604]
  We present MMRPhys, an efficient dual-branch 3D-CNN architecture designed for simultaneous estimation of photoplethysmography (rRSP) and respiratory (rRSP) signals from multimodal video inputs.<n>We demonstrate that MMRPhys with TSFM significantly outperforms state-of-the-art methods in generalization across domain shifts for rRSP estimation, while maintaining a minimal inference latency suitable for real-time applications.
  arXiv  Detail & Related papers  (2025-05-11T15:20:45Z)
• PhysLLM: Harnessing Large Language Models for Cross-Modal Remote Physiological Sensing [49.243031514520794]
  Large Language Models (LLMs) excel at capturing long-range signals due to their text-centric design.<n>PhysLLM achieves state-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios.
  arXiv  Detail & Related papers  (2025-05-06T15:18:38Z)
• FreSca: Scaling in Frequency Space Enhances Diffusion Models [55.75504192166779]
  This paper explores frequency-based control within latent diffusion models.<n>We introduce FreSca, a novel framework that decomposes noise difference into low- and high-frequency components.<n>FreSca operates without any model retraining or architectural change, offering model- and task-agnostic control.
  arXiv  Detail & Related papers  (2025-04-02T22:03:11Z)
• Frequency Domain Enhanced U-Net for Low-Frequency Information-Rich Image Segmentation in Surgical and Deep-Sea Exploration Robots [34.28684917337352]
  We address the differences in frequency band sensitivity between CNNs and the human visual system.<n>We propose a wavelet adaptive spectrum fusion (WASF) method inspired by biological vision mechanisms to balance cross-frequency image features.<n>We develop the FE-UNet model, which employs a SAM2 backbone network and incorporates fine-tuned Hiera-Large modules to ensure segmentation accuracy.
  arXiv  Detail & Related papers  (2025-02-06T07:24:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.