Multi-view feature High-order Fusion for Space Weak Object Detection and Segmentation

• URL: http://arxiv.org/abs/2606.15118v1
• Date: Sat, 13 Jun 2026 05:11:57 GMT
• Title: Multi-view feature High-order Fusion for Space Weak Object Detection and Segmentation
• Abstract summary: We develop simple multi-view attentions, treating their outputs as multi-view features.<n>We also propose a multi-view feature high-order fusion method (MHF) to aggregate more accurate and richer features of weak objects.
• Score: 12.123374796487155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weak objects are common in images and videos of space applications. However, it is hard to learn proper representations from their limited appearance information. Inspired by multi-view learning, we develop simple multi-view attentions, treating their outputs as multi-view features. We also propose a multi-view feature high-order fusion method (MHF) to aggregate more accurate and richer features of weak objects. Our MHF extends the commonly used low-order feature fusion method to higher orders. It enhances the model's capacity to capture relevant and complementary information about weak objects. This is achieved by introducing high-order multi-view features perception and a recursive task-contribution gated selection of multi-view features. The new operation is highly flexible and customizable. It is compatible with various variants of multi-view feature representations. We conduct extensive experiments on two newly constructed space science datasets and an open, large-scale satellite video dataset. Our MHF serves as a plug-and-play module and significantly improves various vision transformers and convolution-based detection and segmentation models. We achieve all state-of-the-art accuracies on both tasks across three datasets. Our MHF can be a new basic module for visual modeling that effectively represents weak objects in terms of multi-view learning. The code will be available at https://github.com/Kingdroper/MHF.

Related papers

• MIMONet: Multi-scale Input and Multi-scale Output Network for Salient Object Detection [68.66589458673157]
  Existing methods for saliency detection focus on the application of multi-level features.<n>We propose a Multi-scale Input and Multi-scale Output Network (MIMONet) to improve the recognition ability of a model for objects with different sizes.
  arXiv  Detail & Related papers  (2026-08-26T12:46:22Z)
• Mixture of Enhanced-View Experts for Multi-Query Vehicle ReID and A Large-Scale Benchmark [24.421311632428043]
  Multi-query vehicle ReID aims to leverage complementary information from diverse views for robust feature learning.<n>This work presents a novel approach called Mixture of Enhanced-View Experts (EV-MoE), which enhances the feature representation of each view.<n>In particular, we design a mixture of enhanced-view experts module, which consists of two parts including view-specific feature enhancement sub-Module (VFEM) and dynamic multi-view fusion sub-Module (DMFM)
  arXiv  Detail & Related papers  (2026-07-09T03:41:36Z)
• Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models [79.59567114769513]
  We introduce Migician, the first multi-image grounding model capable of performing free-form and accurate grounding across multiple images.<n>Our model achieves significantly superior multi-image grounding capabilities, outperforming the best existing MLLMs by 24.94% and even surpassing much larger 70B models.
  arXiv  Detail & Related papers  (2025-01-10T07:56:23Z)
• SM3Det: A Unified Model for Multi-Modal Remote Sensing Object Detection [79.58755811919366]
  This paper introduces a new task called Multi-Modal datasets and Multi-Task Object Detection (M2Det) for remote sensing.<n>It is designed to accurately detect horizontal or oriented objects from any sensor modality.<n>This task poses challenges due to 1) the trade-offs involved in managing multi-modal modelling and 2) the complexities of multi-task optimization.
  arXiv  Detail & Related papers  (2024-12-30T02:47:51Z)
• Instruction-Guided Fusion of Multi-Layer Visual Features in Large Vision-Language Models [50.98559225639266]
  We investigate the contributions of visual features from different encoder layers using 18 benchmarks spanning 6 task categories.<n>Our findings reveal that multilayer features provide complementary strengths with varying task dependencies, and uniform fusion leads to suboptimal performance.<n>We propose the instruction-guided vision aggregator, a module that dynamically integrates multi-layer visual features based on textual instructions.
  arXiv  Detail & Related papers  (2024-12-26T05:41:31Z)
• MMFuser: Multimodal Multi-Layer Feature Fuser for Fine-Grained Vision-Language Understanding [39.68348330596116]
  We propose modelname, a simple yet effective multi-layer feature fuser that efficiently integrates deep and shallow features from Vision Transformers (ViTs) Specifically, it leverages semantically aligned deep features as queries to dynamically extract missing details from shallow features. modelnameachieves significant improvements in visual representation and benchmark performance.
  arXiv  Detail & Related papers  (2024-10-15T17:55:22Z)
• TWIST & SCOUT: Grounding Multimodal LLM-Experts by Forget-Free Tuning [54.033346088090674]
  We introduce TWIST & SCOUT, a framework that equips pre-trained MLLMs with visual grounding ability.<n>To fine-tune the model effectively, we generate a high-quality synthetic dataset we call SCOUT.<n>This dataset provides rich supervision signals, describing a step-by-step multimodal reasoning process.
  arXiv  Detail & Related papers  (2024-10-14T13:35:47Z)
• SCA-PVNet: Self-and-Cross Attention Based Aggregation of Point Cloud and Multi-View for 3D Object Retrieval [8.74845857766369]
  Multi-modality 3D object retrieval is rarely developed and analyzed on large-scale datasets. We propose self-and-cross attention based aggregation of point cloud and multi-view images (SCA-PVNet) for 3D object retrieval.
  arXiv  Detail & Related papers  (2023-07-20T05:46:32Z)
• An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training [79.78201886156513]
  We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks.
  arXiv  Detail & Related papers  (2023-06-29T17:59:57Z)
• MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection [10.04773536815808]
  We propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of finding indistinct objects in images. MFFN captures critical edge and semantic information by comparing and fusing extracted multi-view features. Our method performs favorably against existing state-of-the-art methods via training with the same data.
  arXiv  Detail & Related papers  (2022-10-12T16:12:58Z)
• Multi-modal Transformers Excel at Class-agnostic Object Detection [105.10403103027306]
  We argue that existing methods lack a top-down supervision signal governed by human-understandable semantics. We develop an efficient and flexible MViT architecture using multi-scale feature processing and deformable self-attention. We show the significance of MViT proposals in a diverse range of applications.
  arXiv  Detail & Related papers  (2021-11-22T18:59:29Z)

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.