Decoupled Motion Representation Learning for Moving Infrared Small Target Detection

• URL: http://arxiv.org/abs/2606.15286v1
• Date: Sat, 13 Jun 2026 12:50:32 GMT
• Title: Decoupled Motion Representation Learning for Moving Infrared Small Target Detection
• Abstract summary: We propose a decoupled motion representation learning framework for moving infrared small target detection.<n>An explicit motion branch is introduced to model globally coherent motion dynamics using pretrained optical flow priors.<n>An implicit motion branch based on deformable feature alignment is designed to capture target-sensitive local motion anomalies.<n>A coherent-motion-guided local anomaly reasoning module is proposed to identify and suppress coherent-motion-induced false responses.
• Score: 4.006240810892944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared small target detection in dynamic scenes remains challenging due to the highly coupled motions among targets, imaging platforms, and dynamic backgrounds. Existing multi-frame methods usually perform implicit temporal modeling, where coherent background dynamics dominate motion correspondence learning, leading to an inherent trade-off between detection and false alarms. In this work, we observe that background motions exhibit strong global coherence, whereas small targets mainly correspond to sparse local motion anomalies. Moreover, many false-alarm responses maintain high consistency with globally coherent motion patterns, indicating that they mainly originate from coherent background dynamics rather than genuine target motions. Based on these observations, we propose a decoupled motion representation learning framework for moving infrared small target detection. Specifically, an explicit motion branch is introduced to model globally coherent motion dynamics using pretrained optical flow priors, together with a structure-preserving self-supervised adaptation strategy for infrared motion correspondence learning. Meanwhile, an implicit motion branch based on deformable feature alignment is designed to capture target-sensitive local motion anomalies under coherent motion guidance. Furthermore, a coherent-motion-guided local anomaly reasoning module is proposed to identify and suppress coherent-motion-induced false responses during localized motion modeling. Extensive experiments on two challenging infrared small target detection benchmarks demonstrate that the proposed method consistently outperforms existing state-of-the-art approaches, particularly in dynamic scenes with complex motions, while maintaining favorable inference efficiency.

Related papers

• DyG$^2$T: Modeling Object Dynamics with 3D Gaussian Temporal-Spatial Particle Graph Transformer [86.4618122245946]
  DyG$2$T is a dynamics modeling framework that infers object motion trajectories by spatially completing and temporally discriminating Key Point representations.<n>Experiments on both synthetic and real-world datasets demonstrate that DyG$2$T achieves accurate dynamics modeling and reasoning.
  arXiv  Detail & Related papers  (2026-08-19T03:44:28Z)
• MotiMotion: Motion-Controlled Video Generation with Visual Reasoning [49.243234645532745]
  We introduce MotiMotion, a framework that reformulates motion control as a reasoning-then-generation problem.<n>To encourage causally grounded and commonsense-consistent interactions, we leverage a training-free vision-language reasoner.<n>To further improve motion naturalness, we propose a confidence-aware control scheme that modulates guidance strength.
  arXiv  Detail & Related papers  (2026-05-21T17:59:36Z)
• Decoupling Ego-Motion from Target Dynamics via Dual-Interval Motion Cues for UAV Detection [0.5371337604556311]
  Object detection from Unmanned Aerial Vehicles (UAVs) is challenged by severe ego-motion, camera jitter, and large scale variations.<n>We propose a vision-only motion-guided detection framework that decouples target motion from camera-induced disturbances.<n>Experiments on the VisDrone-VID dataset demonstrate consistent improvements over a strong YOLOv8 baseline under severe ego-motion.
  arXiv  Detail & Related papers  (2026-05-21T15:18:34Z)
• MotionCFG: Boosting Motion Dynamics via Stochastic Concept Perturbation [60.48914865049489]
  MotionCFG is a framework that enhances motion dynamics by contrasting a target concept with its noise-blured counterparts.<n>Unlike explicit negations, this approach facilitates implicit hard negative guidance without shifting the global semantic identity.<n> MotionCFG consistently improves motion dynamics across state-of-the-art T2V frameworks.
  arXiv  Detail & Related papers  (2026-03-14T18:39:37Z)
• GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry [61.24189040578178]
  We propose a fully learning-based approach that directly infers moving objects from latent feature representations via attention mechanisms.<n>Our key insight is to bypass explicit correspondence estimation and instead let the model learn to implicitly disentangle object and camera motion.<n>Our approach achieves state-of-the-art motion segmentation performance with high efficiency.
  arXiv  Detail & Related papers  (2026-02-25T11:36:33Z)
• MoReact: Generating Reactive Motion from Textual Descriptions [57.642436102978245]
  MoReact is a diffusion-based method designed to disentangle the generation of global trajectories and local motions sequentially.<n>Our experiments, utilizing data adapted from a two-person motion dataset, demonstrate the efficacy of our approach.
  arXiv  Detail & Related papers  (2025-09-28T14:31:41Z)
• Motion-Enhanced Nonlocal Similarity Implicit Neural Representation for Infrared Dim and Small Target Detection [15.114540113830522]
  Infrared dim and small target detection presents a significant challenge due to dynamic multi-frame scenarios and weak target signatures.<n>Traditional low-rank plus sparse models often fail to capture dynamic backgrounds and global spatial-temporal correlations.<n>We propose a novel motion-enhanced nonlocal similarity implicit neural representation framework to address these challenges.
  arXiv  Detail & Related papers  (2025-04-22T07:42:00Z)
• Learning segmentation from point trajectories [79.02153797465326]
  We present a way to train a segmentation network using long-term point trajectories as a supervisory signal to complement optical flow.<n>Our method outperforms the prior art on motion-based segmentation.
  arXiv  Detail & Related papers  (2025-01-21T18:59:53Z)
• MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• A Bioinspired Approach-Sensitive Neural Network for Collision Detection in Cluttered and Dynamic Backgrounds [19.93930316898735]
  Rapid accurate and robust detection of looming objects in moving backgrounds is a significant and challenging problem for robotic visual systems. Inspired by the neural circuit elementary vision in the mammalian retina, this paper proposes a bioinspired approach-sensitive neural network (AS) The proposed model is able to not only detect collision accurately and robustly in cluttered and dynamic backgrounds but also extract more collision information like position and direction, for guiding rapid decision making.
  arXiv  Detail & Related papers  (2021-03-01T09:16:18Z)
• Modelling Drosophila Motion Vision Pathways for Decoding the Direction of Translating Objects Against Cluttered Moving Backgrounds [6.670414650224423]
  This paper investigates the fruit fly textitDrosophila motion vision pathways and presents computational modelling based on physiological researches. The proposed visual system model features-plausible ON and OFF pathways, wide-field horizontal-sensitive (HS) and vertical-sensitive (VS) systems. Experiments have verified the effectiveness of the proposed neural system model, and demonstrated its responsive preference to faster-moving, higher-contrast and larger-size targets.
  arXiv  Detail & Related papers  (2020-07-02T05:15:31Z)

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.