OptiCorNet: Optimizing Sequence-Based Context Correlation for Visual Place Recognition

• URL: http://arxiv.org/abs/2507.14477v1
• Date: Sat, 19 Jul 2025 04:29:43 GMT
• Title: OptiCorNet: Optimizing Sequence-Based Context Correlation for Visual Place Recognition
• Authors: Zhenyu Li, Tianyi Shang, Pengjie Xu, Ruirui Zhang, Fanchen Kong,
• Abstract summary: This paper presents OptiCorNet, a novel sequence modeling framework.<n>It unifies spatial feature extraction and temporal differencing into a differentiable, end-to-end trainable module.<n>Our approach outperforms state-of-the-art baselines under challenging seasonal and viewpoint variations.
• Score: 2.3093110834423616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Place Recognition (VPR) in dynamic and perceptually aliased environments remains a fundamental challenge for long-term localization. Existing deep learning-based solutions predominantly focus on single-frame embeddings, neglecting the temporal coherence present in image sequences. This paper presents OptiCorNet, a novel sequence modeling framework that unifies spatial feature extraction and temporal differencing into a differentiable, end-to-end trainable module. Central to our approach is a lightweight 1D convolutional encoder combined with a learnable differential temporal operator, termed Differentiable Sequence Delta (DSD), which jointly captures short-term spatial context and long-range temporal transitions. The DSD module models directional differences across sequences via a fixed-weight differencing kernel, followed by an LSTM-based refinement and optional residual projection, yielding compact, discriminative descriptors robust to viewpoint and appearance shifts. To further enhance inter-class separability, we incorporate a quadruplet loss that optimizes both positive alignment and multi-negative divergence within each batch. Unlike prior VPR methods that treat temporal aggregation as post-processing, OptiCorNet learns sequence-level embeddings directly, enabling more effective end-to-end place recognition. Comprehensive evaluations on multiple public benchmarks demonstrate that our approach outperforms state-of-the-art baselines under challenging seasonal and viewpoint variations.

Related papers

• DAMS:Dual-Branch Adaptive Multiscale Spatiotemporal Framework for Video Anomaly Detection [7.117824587276951]
  This study offers a dual-path architecture called the Dual-Branch Adaptive Multiscale Stemporal Framework (DAMS), which is based on multilevel feature and decoupling fusion.<n>The main processing path integrates the Adaptive Multiscale Time Pyramid Network (AMTPN) with the Convolutional Block Attention Mechanism (CBAM)
  arXiv  Detail & Related papers  (2025-07-28T08:42:00Z)
• IPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental Segmentation [77.06177202334398]
  We identify two critical challenges in CISS that contribute to semantic drift and degrade performance.<n>First, we highlight the issue of separate optimization, where different parts of the model are optimized in distinct incremental stages.<n>Second, we identify noisy semantics arising from inappropriate pseudo-labeling, which results in sub-optimal results.
  arXiv  Detail & Related papers  (2025-02-07T12:19:37Z)
• Multi-Modality Driven LoRA for Adverse Condition Depth Estimation [61.525312117638116]
  We propose Multi-Modality Driven LoRA (MMD-LoRA) for Adverse Condition Depth Estimation.<n>It consists of two core components: Prompt Driven Domain Alignment (PDDA) and Visual-Text Consistent Contrastive Learning (VTCCL)<n>It achieves state-of-the-art performance on the nuScenes and Oxford RobotCar datasets.
  arXiv  Detail & Related papers  (2024-12-28T14:23:58Z)
• OED: Towards One-stage End-to-End Dynamic Scene Graph Generation [18.374354844446962]
  Dynamic Scene Graph Generation (DSGG) focuses on identifying visual relationships within the spatial-temporal domain of videos. We propose a one-stage end-to-end framework, termed OED, which streamlines the DSGG pipeline. This framework reformulates the task as a set prediction problem and leverages pair-wise features to represent each subject-object pair within the scene graph.
  arXiv  Detail & Related papers  (2024-05-27T08:18:41Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences [79.21515035128832]
  We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC) iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains.
  arXiv  Detail & Related papers  (2023-11-06T18:53:01Z)
• FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space [7.324708513042455]
  This paper proposes a novel contrastive learning framework, called FOCAL, for extracting comprehensive features from multimodal time-series sensing signals. It consistently outperforms the state-of-the-art baselines in downstream tasks with a clear margin.
  arXiv  Detail & Related papers  (2023-10-30T22:55:29Z)
• Local-Global Temporal Difference Learning for Satellite Video Super-Resolution [53.03380679343968]
  We propose to exploit the well-defined temporal difference for efficient and effective temporal compensation.<n>To fully utilize the local and global temporal information within frames, we systematically modeled the short-term and long-term temporal discrepancies.<n> Rigorous objective and subjective evaluations conducted across five mainstream video satellites demonstrate that our method performs favorably against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-10T07:04:40Z)
• RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation [3.57686754209902]
  Quantification of retinal fluids is necessary for OCT-guided treatment management. New convolutional neural architecture named RetiFluidNet is proposed for multi-class retinal fluid segmentation. Model benefits from hierarchical representation learning of textural, contextual, and edge features.
  arXiv  Detail & Related papers  (2022-09-26T07:18:00Z)
• Gait Recognition in the Wild with Multi-hop Temporal Switch [81.35245014397759]
  gait recognition in the wild is a more practical problem that has attracted the attention of the community of multimedia and computer vision. This paper presents a novel multi-hop temporal switch method to achieve effective temporal modeling of gait patterns in real-world scenes.
  arXiv  Detail & Related papers  (2022-09-01T10:46:09Z)
• Video Saliency Prediction Using Enhanced Spatiotemporal Alignment Network [35.932447204088845]
  We develop an effective feature alignment network tailored to video saliency prediction (V) The network learns to align the features of the neighboring frames to the reference one in a coarse-to-fine manner. The proposed model is trained end-to-end without any post processing.
  arXiv  Detail & Related papers  (2020-01-02T02:05:35Z)

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.