Unified Object Detector for Different Modalities based on Vision Transformers

• URL: http://arxiv.org/abs/2207.01071v2
• Date: Fri, 5 May 2023 23:20:33 GMT
• Title: Unified Object Detector for Different Modalities based on Vision Transformers
• Authors: Xiaoke Shen and Ioannis Stamos
• Abstract summary: We develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors. We evaluate our unified model on the SUN RGB-D dataset, and demonstrate that it achieves similar or better performance in terms of mAP50.
• Score: 1.14219428942199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional systems typically require different models for processing different modalities, such as one model for RGB images and another for depth images. Recent research has demonstrated that a single model for one modality can be adapted for another using cross-modality transfer learning. In this paper, we extend this approach by combining cross/inter-modality transfer learning with a vision transformer to develop a unified detector that achieves superior performance across diverse modalities. Our research envisions an application scenario for robotics, where the unified system seamlessly switches between RGB cameras and depth sensors in varying lighting conditions. Importantly, the system requires no model architecture or weight updates to enable this smooth transition. Specifically, the system uses the depth sensor during low-lighting conditions (night time) and both the RGB camera and depth sensor or RGB caemra only in well-lit environments. We evaluate our unified model on the SUN RGB-D dataset, and demonstrate that it achieves similar or better performance in terms of mAP50 compared to state-of-the-art methods in the SUNRGBD16 category, and comparable performance in point cloud only mode. We also introduce a novel inter-modality mixing method that enables our model to achieve significantly better results than previous methods. We provide our code, including training/inference logs and model checkpoints, to facilitate reproducibility and further research. \url{https://github.com/liketheflower/UODDM}

Related papers

• MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection [12.462709547836289]
  Using multiple modalities like visible (RGB) and infrared (IR) can greatly improve the performance of a predictive task such as object detection (OD) In this paper, we tackle a different way to employ RGB and IR modalities, where only one modality or the other is observed by a single shared vision encoder. This work investigates how to efficiently leverage RGB and IR modalities to train a common transformer-based OD vision encoder, while countering the effects of modality imbalance.
  arXiv  Detail & Related papers  (2024-04-29T16:42:58Z)
• Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge [11.905387325966311]
  This paper focuses on adapting a large object detection model trained on RGB images to new data extracted from IR images. We propose Modality Translator (ModTr) as an alternative to the common approach of fine-tuning a large model to the new modality.
  arXiv  Detail & Related papers  (2024-04-01T21:28:50Z)
• Bi-directional Adapter for Multi-modal Tracking [67.01179868400229]
  We propose a novel multi-modal visual prompt tracking model based on a universal bi-directional adapter. We develop a simple but effective light feature adapter to transfer modality-specific information from one modality to another. Our model achieves superior tracking performance in comparison with both the full fine-tuning methods and the prompt learning-based methods.
  arXiv  Detail & Related papers  (2023-12-17T05:27:31Z)
• RBF Weighted Hyper-Involution for RGB-D Object Detection [0.0]
  We propose a real-time and two stream RGBD object detection model. The proposed model consists of two new components: a depth guided hyper-involution that adapts dynamically based on the spatial interaction pattern in the raw depth map and an up-sampling based trainable fusion layer. We show that the proposed model outperforms other RGB-D based object detection models on NYU Depth v2 dataset and achieves comparable (second best) results on SUN RGB-D.
  arXiv  Detail & Related papers  (2023-09-30T11:25:34Z)
• CoMAE: Single Model Hybrid Pre-training on Small-Scale RGB-D Datasets [50.6643933702394]
  We present a single-model self-supervised hybrid pre-training framework for RGB and depth modalities, termed as CoMAE. Our CoMAE presents a curriculum learning strategy to unify the two popular self-supervised representation learning algorithms: contrastive learning and masked image modeling.
  arXiv  Detail & Related papers  (2023-02-13T07:09:45Z)
• Students taught by multimodal teachers are superior action recognizers [41.821485757189656]
  The focal point of egocentric video understanding is modelling hand-object interactions. Standard models -- CNNs, Vision Transformers, etc. -- which receive RGB frames as input perform well, however, their performance improves further by employing additional modalities such as object detections, optical flow, audio, etc. The goal of this work is to retain the performance of such multimodal approaches, while using only the RGB images as input at inference time.
  arXiv  Detail & Related papers  (2022-10-09T19:37:17Z)
• A Strong Transfer Baseline for RGB-D Fusion in Vision Transformers [0.0]
  We propose a recipe for transferring pretrained ViTs in RGB-D domains for single-view 3D object recognition. We show that our adapted ViTs score up to 95.1% top-1 accuracy in Washington, achieving new state-of-the-art results in this benchmark.
  arXiv  Detail & Related papers  (2022-10-03T12:08:09Z)
• Dual Swin-Transformer based Mutual Interactive Network for RGB-D Salient Object Detection [67.33924278729903]
  In this work, we propose Dual Swin-Transformer based Mutual Interactive Network. We adopt Swin-Transformer as the feature extractor for both RGB and depth modality to model the long-range dependencies in visual inputs. Comprehensive experiments on five standard RGB-D SOD benchmark datasets demonstrate the superiority of the proposed DTMINet method.
  arXiv  Detail & Related papers  (2022-06-07T08:35:41Z)
• Visual Saliency Transformer [127.33678448761599]
  We develop a novel unified model based on a pure transformer, Visual Saliency Transformer (VST), for both RGB and RGB-D salient object detection (SOD) It takes image patches as inputs and leverages the transformer to propagate global contexts among image patches. Experimental results show that our model outperforms existing state-of-the-art results on both RGB and RGB-D SOD benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-25T08:24:06Z)
• 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)
• A Single Stream Network for Robust and Real-time RGB-D Salient Object Detection [89.88222217065858]
  We design a single stream network to use the depth map to guide early fusion and middle fusion between RGB and depth. This model is 55.5% lighter than the current lightest model and runs at a real-time speed of 32 FPS when processing a $384 times 384$ image.
  arXiv  Detail & Related papers  (2020-07-14T04:40:14Z)

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.