BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation

• URL: http://arxiv.org/abs/2506.03675v1
• Date: Wed, 04 Jun 2025 08:04:58 GMT
• Title: BiXFormer: A Robust Framework for Maximizing Modality Effectiveness in Multi-Modal Semantic Segmentation
• Authors: Jialei Chen, Xu Zheng, Danda Pani Paudel, Luc Van Gool, Hiroshi Murase, Daisuke Deguchi,
• Abstract summary: We reformulate multi-modal semantic segmentation as a mask-level classification task.<n>We propose BiXFormer, which integrates Unified Modality Matching (UMM) and Cross Modality Alignment (CMA)<n> Experiments on both synthetic and real-world multi-modal benchmarks demonstrate the effectiveness of our method.
• Score: 55.486872677160015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Utilizing multi-modal data enhances scene understanding by providing complementary semantic and geometric information. Existing methods fuse features or distill knowledge from multiple modalities into a unified representation, improving robustness but restricting each modality's ability to fully leverage its strengths in different situations. We reformulate multi-modal semantic segmentation as a mask-level classification task and propose BiXFormer, which integrates Unified Modality Matching (UMM) and Cross Modality Alignment (CMA) to maximize modality effectiveness and handle missing modalities. Specifically, BiXFormer first categorizes multi-modal inputs into RGB and X, where X represents any non-RGB modalities, e.g., depth, allowing separate processing for each. This design leverages the well-established pretraining for RGB, while addressing the relative lack of attention to X modalities. Then, we propose UMM, which includes Modality Agnostic Matching (MAM) and Complementary Matching (CM). MAM assigns labels to features from all modalities without considering modality differences, leveraging each modality's strengths. CM then reassigns unmatched labels to remaining unassigned features within their respective modalities, ensuring that each available modality contributes to the final prediction and mitigating the impact of missing modalities. Moreover, to further facilitate UMM, we introduce CMA, which enhances the weaker queries assigned in CM by aligning them with optimally matched queries from MAM. Experiments on both synthetic and real-world multi-modal benchmarks demonstrate the effectiveness of our method, achieving significant improvements in mIoU of +2.75% and +22.74% over the prior arts.

Related papers

• EGFormer: Towards Efficient and Generalizable Multimodal Semantic Segmentation [6.314084134346798]
  EGFormer is an efficient multimodal semantic segmentation framework.<n>It flexibly integrates an arbitrary number of modalities while significantly reducing model parameters and inference time.<n>It achieves competitive performance with up to 88 percent reduction in parameters and 50 percent fewer GFLOPs.
  arXiv  Detail & Related papers  (2025-05-20T07:08:49Z)
• Learning Item Representations Directly from Multimodal Features for Effective Recommendation [51.49251689107541]
  multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations.<n>We propose a novel model (i.e., LIRDRec) that learns item representations directly from multimodal features to augment recommendation performance.
  arXiv  Detail & Related papers  (2025-05-08T05:42:22Z)
• MAGIC++: Efficient and Resilient Modality-Agnostic Semantic Segmentation via Hierarchical Modality Selection [20.584588303521496]
  We introduce the MAGIC++ framework, which comprises two key plug-and-play modules for effective multi-modal fusion and hierarchical modality selection.<n>Our method achieves state-of-the-art performance on both real-world and synthetic benchmarks.<n>Our method is superior in the novel modality-agnostic setting, where it outperforms prior arts by a large margin.
  arXiv  Detail & Related papers  (2024-12-22T06:12:03Z)
• MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment [7.109735168520378]
  Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the specificity of each modality. We propose the Multi-modal Consistency and Specificity Fusion Framework (MCSFF), which innovatively integrates both complementary and specific aspects of modalities.
  arXiv  Detail & Related papers  (2024-10-18T16:35:25Z)
• Learning Modality-agnostic Representation for Semantic Segmentation from Any Modalities [8.517830626176641]
  Any2Seg is a novel framework that can achieve robust segmentation from any combination of modalities in any visual conditions. Experiments on two benchmarks with four modalities demonstrate that Any2Seg achieves the state-of-the-art under the multi-modal setting.
  arXiv  Detail & Related papers  (2024-07-16T03:34:38Z)
• Centering the Value of Every Modality: Towards Efficient and Resilient Modality-agnostic Semantic Segmentation [7.797154022794006]
  Recent endeavors regard RGB modality as the center and the others as the auxiliary, yielding an asymmetric architecture with two branches. We propose a novel method, named MAGIC, that can be flexibly paired with various backbones, ranging from compact to high-performance models. Our method achieves state-of-the-art performance while reducing the model parameters by 60%.
  arXiv  Detail & Related papers  (2024-07-16T03:19:59Z)
• Modality Prompts for Arbitrary Modality Salient Object Detection [57.610000247519196]
  This paper delves into the task of arbitrary modality salient object detection (AM SOD) It aims to detect salient objects from arbitrary modalities, eg RGB images, RGB-D images, and RGB-D-T images. A novel modality-adaptive Transformer (MAT) will be proposed to investigate two fundamental challenges of AM SOD.
  arXiv  Detail & Related papers  (2024-05-06T11:02:02Z)
• NativE: Multi-modal Knowledge Graph Completion in the Wild [51.80447197290866]
  We propose a comprehensive framework NativE to achieve MMKGC in the wild. NativE proposes a relation-guided dual adaptive fusion module that enables adaptive fusion for any modalities. We construct a new benchmark called WildKGC with five datasets to evaluate our method.
  arXiv  Detail & Related papers  (2024-03-28T03:04:00Z)
• Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding [62.70450216120704]
  Unsupervised pre-training has shown great success in skeleton-based action understanding. We propose a Unified Multimodal Unsupervised Representation Learning framework, called UmURL. UmURL exploits an efficient early-fusion strategy to jointly encode the multi-modal features in a single-stream manner.
  arXiv  Detail & Related papers  (2023-11-06T13:56:57Z)
• Exploiting modality-invariant feature for robust multimodal emotion recognition with missing modalities [76.08541852988536]
  We propose to use invariant features for a missing modality imagination network (IF-MMIN) We show that the proposed model outperforms all baselines and invariantly improves the overall emotion recognition performance under uncertain missing-modality conditions.
  arXiv  Detail & Related papers  (2022-10-27T12:16:25Z)

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.