EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization

• URL: http://arxiv.org/abs/2608.09656v1
• Date: Mon, 10 Aug 2026 14:31:01 GMT
• Title: EgoHieraLoc: A Cortically Inspired Hierarchical Segmentation-Guided Framework for Egocentric Visual Query Localization
• Abstract summary: Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos.<n>Human vision handles ambiguity through a hierarchical process.<n>We propose textbfEgoHieraLoc, a unified framework for VQL-2D and VQL-3D.
• Score: 9.547789251222838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual query localization (VQL) aims to retrieve and re-localize a queried object in egocentric videos, yet remains challenging when object boundaries are ambiguous and global context cannot effectively guide fine-grained localization. Human vision handles such ambiguity through a hierarchical process: it rapidly screens foreground candidates, selectively attends to the target despite distractors, refines perception via feedback between global context and local detail, and, when a single view is unreliable, integrates evidence across viewpoints according to its credibility. Inspired by these competencies, we propose \textbf{EgoHieraLoc}, a unified framework for VQL-2D and VQL-3D. A Discriminative Parsing Module first extracts foreground-aware query representations using segmentation priors; a Query-Aware Module then performs robust target localization through discriminative correlation filtering with deformable modeling; and a Regional Adaptation Module feeds multi-scale context back into local regions to recover precise object boundaries. To extend this perceptual hierarchy to 3D localization, we introduce Geometric-Semantic Joint Confidence (GSJC), which multiplicatively couples segmentation confidence with local depth consistency, multi-view back-projection consistency, and triangulation-baseline quality, so that a viewpoint contributes to the 3D estimate only when it is credible both semantically and geometrically. Extensive experiments demonstrate state-of-the-art performance on both VQL-2D and -3D benchmarks.

Related papers

• From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation [40.51712647240084]
  UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments.<n>Current approaches suffer from three coupled issues: weak grounding of instruction-relevant landmarks in visual observations, insufficient exploitation of long-horizon history, and unstable decisions under local traps or repeated exploration.<n>We propose a unified semantic-to-decision framework to address these issues.
  arXiv  Detail & Related papers  (2026-08-10T12:59:50Z)
• Compact Object-Level Representations with Open-Vocabulary Understanding for Indoor Visual Relocalization [93.79141221821858]
  OpenReLoc is a camera relocalization system designed to provide scene understanding and accurate pose estimation capabilities.<n>We propose a multi-modal mechanism to integrate open-vocabulary semantic knowledge for effective 2D-3D object matching.<n> Experimental results demonstrate that OpenReLoc achieves superior relocalization recall and accuracy across various datasets.
  arXiv  Detail & Related papers  (2026-06-23T16:27:04Z)
• IGV-RRT: Prior-Real-Time Observation Fusion for Active Object Search in Changing Environments [10.748685496551067]
  We propose a probabilistic planning framework that combines uncertainty-aware scene priors with online target relevance estimates.<n>The framework contains a dual-layer semantic mapping module and a real-time planner.<n>The proposed method effectively mitigates the impact of object rearrangement, achieving higher search efficiency and success rates.
  arXiv  Detail & Related papers  (2026-03-23T12:14:26Z)
• PC-CrossDiff: Point-Cluster Dual-Level Cross-Modal Differential Attention for Unified 3D Referring and Segmentation [40.5034963034718]
  3D Visual Grounding aims to localize the referent of natural language referring expressions through two core tasks: Referring Expression (3DREC) and (3DRES)<n>Existing methods face two key challenges in complex, multi-object scenes: inadequate parsing of implicit localization cues and ineffective suppression of dynamic spatial interference from co-occurring objects.<n>We propose PC-CrossDiff, a unified dual-task framework with a dual-level cross-modal differential attention architecture for 3DREC and 3DRES.
  arXiv  Detail & Related papers  (2026-03-18T14:16:48Z)
• Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization [8.559240391514063]
  Cross-view object geo-localization enables high-precision object localization through cross-view matching.<n>Existing methods rely on keypoint-based positional encoding, which captures only 2D coordinates while neglecting object shape information.<n>We propose a mask-based positional encoding scheme that leverages segmentation masks to capture both spatial coordinates and object silhouettes.<n>We present EDGeo, an end-to-end framework for robust cross-view object geo-localization.
  arXiv  Detail & Related papers  (2025-10-23T06:07:07Z)
• Generalized Decoupled Learning for Enhancing Open-Vocabulary Dense Perception [71.26728044621458]
  DeCLIP is a novel framework that enhances CLIP by decoupling the self-attention module to obtain content'' and context'' features respectively.<n>It consistently achieves state-of-the-art performance across a broad spectrum of tasks, including 2D detection and segmentation, 3D instance segmentation, video instance segmentation, and 6D object pose estimation.
  arXiv  Detail & Related papers  (2025-08-15T06:43:51Z)
• Hierarchical Scoring with 3D Gaussian Splatting for Instance Image-Goal Navigation [27.040017548286812]
  Instance Image-Goal Navigation (IIN) requires autonomous agents to identify and navigate to a target object or location depicted in a reference image captured from any viewpoint.<n>We introduce a novel IIN framework with a hierarchical scoring paradigm that estimates optimal viewpoints for target matching.
  arXiv  Detail & Related papers  (2025-06-09T00:58:14Z)
• EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting [108.15136508964011]
  EgoSplat is a language-embedded 3D Gaussian Splatting framework for open-vocabulary egocentric scene understanding.<n>EgoSplat achieves state-of-the-art performance in both localization and segmentation tasks on two datasets.
  arXiv  Detail & Related papers  (2025-03-14T12:21:26Z)
• Hierarchical Temporal Context Learning for Camera-based Semantic Scene Completion [57.232688209606515]
  We present HTCL, a novel Temporal Temporal Context Learning paradigm for improving camera-based semantic scene completion. Our method ranks $1st$ on the Semantic KITTI benchmark and even surpasses LiDAR-based methods in terms of mIoU.
  arXiv  Detail & Related papers  (2024-07-02T09:11:17Z)
• MMScan: A Multi-Modal 3D Scene Dataset with Hierarchical Grounded Language Annotations [55.022519020409405]
  This paper builds the first largest ever multi-modal 3D scene dataset and benchmark with hierarchical grounded language annotations, MMScan.<n>The resulting multi-modal 3D dataset encompasses 1.4M meta-annotated captions on 109k objects and 7.7k regions as well as over 3.04M diverse samples for 3D visual grounding and question-answering benchmarks.
  arXiv  Detail & Related papers  (2024-06-13T17:59:30Z)
• LCPFormer: Towards Effective 3D Point Cloud Analysis via Local Context Propagation in Transformers [60.51925353387151]
  We propose a novel module named Local Context Propagation (LCP) to exploit the message passing between neighboring local regions. We use the overlap points of adjacent local regions as intermediaries, then re-weight the features of these shared points from different local regions before passing them to the next layers. The proposed method is applicable to different tasks and outperforms various transformer-based methods in benchmarks including 3D shape classification and dense prediction tasks.
  arXiv  Detail & Related papers  (2022-10-23T15:43:01Z)
• InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring [38.13420293700949]
  We propose a new model, named InstanceRefer, to achieve a superior 3D visual grounding on point clouds. Our model first filters instances from panoptic segmentation on point clouds to obtain a small number of candidates. Experiments confirm that our InstanceRefer outperforms previous state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2021-03-01T16:59:27Z)

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.