Mixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark

• URL: http://arxiv.org/abs/2410.12143v2
• Date: Fri, 18 Apr 2025 07:11:49 GMT
• Title: Mixture of Scale Experts for Alignment-free RGBT Video Object Detection and A Unified Benchmark
• Authors: Qishun Wang, Zhengzheng Tu, Kunpeng Wang, Le Gu, Chuanwang Guo,
• Abstract summary: Existing RGB-Thermal Video Object Detection (RGBT VOD) methods rely on the manual alignment of image pairs.<n>We propose a novel framework termed the Mixture of Scale Experts Network (MSENet)<n>MSENet integrates multiple experts trained at different perceptual scales, enabling the capture of scale discrepancies between RGB and thermal image pairs.
• Score: 5.068440399797739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing RGB-Thermal Video Object Detection (RGBT VOD) methods predominantly rely on the manual alignment of image pairs, that is both labor-intensive and time-consuming. This dependency significantly restricts the scalability and practical applicability of these methods in real-world scenarios. To address this critical limitation, we propose a novel framework termed the Mixture of Scale Experts Network (MSENet). MSENet integrates multiple experts trained at different perceptual scales, enabling the capture of scale discrepancies between RGB and thermal image pairs without the need for explicit alignment. Specifically, to address the issue of unaligned scales, MSENet introduces a set of experts designed to perceive the correlation between RGBT image pairs across various scales. These experts are capable of identifying and quantifying the scale differences inherent in the image pairs. Subsequently, a dynamic routing mechanism is incorporated to assign adaptive weights to each expert, allowing the network to dynamically select the most appropriate experts based on the specific characteristics of the input data. Furthermore, to address the issue of weakly unaligned positions, we integrate deformable convolution into the network. Deformable convolution is employed to learn position displacements between the RGB and thermal modalities, thereby mitigating the impact of spatial misalignment. To provide a comprehensive evaluation platform for alignment-free RGBT VOD, we introduce a new benchmark dataset. This dataset includes eleven common object categories, with a total of 60,988 images and 271,835 object instances. The dataset encompasses a wide range of scenes from both daily life and natural environments, ensuring high content diversity and complexity.

Related papers

• A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding [76.44979557843367]
  We propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. We introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information. We explicitly estimate the quality of the current pixel corresponding to sampled points on the epipolar line of the source image.
  arXiv  Detail & Related papers  (2024-11-04T08:50:16Z)
• Monocular Visual Place Recognition in LiDAR Maps via Cross-Modal State Space Model and Multi-View Matching [2.400446821380503]
  We introduce an efficient framework to learn descriptors for both RGB images and point clouds. It takes visual state space model (VMamba) as the backbone and employs a pixel-view-scene joint training strategy. A visible 3D points overlap strategy is then designed to quantify the similarity between point cloud views and RGB images for multi-view supervision.
  arXiv  Detail & Related papers  (2024-10-08T18:31:41Z)
• Diffusion-based RGB-D Semantic Segmentation with Deformable Attention Transformer [10.982521876026281]
  We introduce a diffusion-based framework to address the RGB-D semantic segmentation problem. We demonstrate that utilizing a Deformable Attention Transformer as the encoder to extract features from depth images effectively captures the characteristics of invalid regions in depth measurements.
  arXiv  Detail & Related papers  (2024-09-23T15:23:01Z)
• Depth-Guided Semi-Supervised Instance Segmentation [62.80063539262021]
  Semi-Supervised Instance (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled images to generate pseudo-labels. We introduce a Depth-Guided (DG) framework to overcome this limitation.
  arXiv  Detail & Related papers  (2024-06-25T09:36:50Z)
• ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection [51.16181295385818]
  We first collect an annotated RGB-D video SODOD (DSOD-100) dataset, which contains 100 videos within a total of 9,362 frames. All the frames in each video are manually annotated to a high-quality saliency annotation. We propose a new baseline model, named attentive triple-fusion network (ATF-Net) for RGB-D salient object detection.
  arXiv  Detail & Related papers  (2024-06-18T12:09:43Z)
• Segment Any Events via Weighted Adaptation of Pivotal Tokens [85.39087004253163]
  This paper focuses on the nuanced challenge of tailoring the Segment Anything Models (SAMs) for integration with event data. We introduce a multi-scale feature distillation methodology to optimize the alignment of token embeddings originating from event data with their RGB image counterparts.
  arXiv  Detail & Related papers  (2023-12-24T12:47:08Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• RGM: A Robust Generalizable Matching Model [49.60975442871967]
  We propose a deep model for sparse and dense matching, termed RGM (Robust Generalist Matching) To narrow the gap between synthetic training samples and real-world scenarios, we build a new, large-scale dataset with sparse correspondence ground truth. We are able to mix up various dense and sparse matching datasets, significantly improving the training diversity.
  arXiv  Detail & Related papers  (2023-10-18T07:30:08Z)
• Unified Frequency-Assisted Transformer Framework for Detecting and Grounding Multi-Modal Manipulation [109.1912721224697]
  We present the Unified Frequency-Assisted transFormer framework, named UFAFormer, to address the DGM4 problem. By leveraging the discrete wavelet transform, we decompose images into several frequency sub-bands, capturing rich face forgery artifacts. Our proposed frequency encoder, incorporating intra-band and inter-band self-attentions, explicitly aggregates forgery features within and across diverse sub-bands.
  arXiv  Detail & Related papers  (2023-09-18T11:06:42Z)
• Mutual-Guided Dynamic Network for Image Fusion [51.615598671899335]
  We propose a novel mutual-guided dynamic network (MGDN) for image fusion, which allows for effective information utilization across different locations and inputs. Experimental results on five benchmark datasets demonstrate that our proposed method outperforms existing methods on four image fusion tasks.
  arXiv  Detail & Related papers  (2023-08-24T03:50:37Z)
• Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation [19.41334573257174]
  Traditional methods mostly use RGB images which are heavily affected by lighting conditions, eg, darkness. Recent studies show thermal images are robust to the night scenario as a compensating modality for segmentation. This work proposes a Residual Spatial Fusion Network (RSFNet) for RGB-T semantic segmentation.
  arXiv  Detail & Related papers  (2023-06-17T14:28:08Z)
• Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning [19.346284003982035]
  We propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods.
  arXiv  Detail & Related papers  (2023-03-13T17:45:39Z)
• Group Contextualization for Video Recognition [80.3842253625557]
  Group contextualization (GC) can boost the performance of 2D-CNN (e.g., TSN) and TSM. GC embeds feature with four different kinds of contexts in parallel. Group contextualization can boost the performance of 2D-CNN (e.g., TSN) to a level comparable to the state-the-art video networks.
  arXiv  Detail & Related papers  (2022-03-18T01:49:40Z)
• TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization [49.521622399483846]
  We propose a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) for generic image manipulation localization. The proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.
  arXiv  Detail & Related papers  (2021-08-10T08:22:05Z)
• End-to-end Multi-modal Video Temporal Grounding [105.36814858748285]
  We propose a multi-modal framework to extract complementary information from videos. We adopt RGB images for appearance, optical flow for motion, and depth maps for image structure. We conduct experiments on the Charades-STA and ActivityNet Captions datasets, and show that the proposed method performs favorably against state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-07-12T17:58:10Z)
• RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss [37.99375824040946]
  We propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target representation learning. Experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker.
  arXiv  Detail & Related papers  (2020-11-14T01:50:46Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)
• Multi-Granularity Canonical Appearance Pooling for Remote Sensing Scene Classification [0.34376560669160383]
  We propose a novel Multi-Granularity Canonical Appearance Pooling (MG-CAP) to automatically capture the latent ontological structure of remote sensing datasets. For each specific granularity, we discover the canonical appearance from a set of pre-defined transformations and learn the corresponding CNN features through a maxout-based Siamese style architecture. We provide a stable solution for training the eigenvalue-decomposition function (EIG) in a GPU and demonstrate the corresponding back-propagation using matrix calculus.
  arXiv  Detail & Related papers  (2020-04-09T11:24:00Z)

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.