Multi-scale Alternated Attention Transformer for Generalized Stereo Matching

• URL: http://arxiv.org/abs/2308.03048v1
• Date: Sun, 6 Aug 2023 08:22:39 GMT
• Title: Multi-scale Alternated Attention Transformer for Generalized Stereo Matching
• Authors: Wei Miao, Hong Zhao, Tongjia Chen, Wei Huang, Changyan Xiao
• Abstract summary: We present a simple but highly effective network called Alternated Attention U-shaped Transformer (AAUformer) to balance the impact of epipolar line in dual and single view. Compared to other models, our model has several main designs. We performed a series of both comparative studies and ablation studies on several mainstream stereo matching datasets.
• Score: 7.493797166406228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent stereo matching networks achieves dramatic performance by introducing epipolar line constraint to limit the matching range of dual-view. However, in complicated real-world scenarios, the feature information based on intra-epipolar line alone is too weak to facilitate stereo matching. In this paper, we present a simple but highly effective network called Alternated Attention U-shaped Transformer (AAUformer) to balance the impact of epipolar line in dual and single view respectively for excellent generalization performance. Compared to other models, our model has several main designs: 1) to better liberate the local semantic features of the single-view at pixel level, we introduce window self-attention to break the limits of intra-row self-attention and completely replace the convolutional network for denser features before cross-matching; 2) the multi-scale alternated attention backbone network was designed to extract invariant features in order to achieves the coarse-to-fine matching process for hard-to-discriminate regions. We performed a series of both comparative studies and ablation studies on several mainstream stereo matching datasets. The results demonstrate that our model achieves state-of-the-art on the Scene Flow dataset, and the fine-tuning performance is competitive on the KITTI 2015 dataset. In addition, for cross generalization experiments on synthetic and real-world datasets, our model outperforms several state-of-the-art works.

Related papers

• Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition [57.74076383449153]
  We propose a novel dual-stream framework for event stream-based pattern recognition via differentiated fusion, termed EFV++. It models two common event representations simultaneously, i.e., event images and event voxels. We achieve new state-of-the-art performance on the Bullying10k dataset, i.e., $90.51%$, which exceeds the second place by $+2.21%$.
  arXiv  Detail & Related papers  (2024-06-27T02:32:46Z)
• DeblurDiNAT: A Generalizable Transformer for Perceptual Image Deblurring [1.5124439914522694]
  DeblurDiNAT is a generalizable and efficient encoder-decoder Transformer which restores clean images visually close to the ground truth. We present a linear feed-forward network and a non-linear dual-stage feature fusion module for faster feature propagation across the network.
  arXiv  Detail & Related papers  (2024-03-19T21:31:31Z)
• DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
  We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
  arXiv  Detail & Related papers  (2023-08-17T14:14:28Z)
• Unifying Flow, Stereo and Depth Estimation [121.54066319299261]
  We present a unified formulation and model for three motion and 3D perception tasks. We formulate all three tasks as a unified dense correspondence matching problem. Our model naturally enables cross-task transfer since the model architecture and parameters are shared across tasks.
  arXiv  Detail & Related papers  (2022-11-10T18:59:54Z)
• Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation [5.281694565226513]
  We apply contrastive learning to enhance the discriminative power of the multi-scale features extracted by semantic segmentation networks. By first mapping the encoder's multi-scale representations to a common feature space, we instantiate a novel form of supervised local-global constraint.
  arXiv  Detail & Related papers  (2022-03-25T01:24:24Z)
• ViTAEv2: Vision Transformer Advanced by Exploring Inductive Bias for Image Recognition and Beyond [76.35955924137986]
  We propose a Vision Transformer Advanced by Exploring intrinsic IB from convolutions, i.e., ViTAE. ViTAE has several spatial pyramid reduction modules to downsample and embed the input image into tokens with rich multi-scale context. We obtain the state-of-the-art classification performance, i.e., 88.5% Top-1 classification accuracy on ImageNet validation set and the best 91.2% Top-1 accuracy on ImageNet real validation set.
  arXiv  Detail & Related papers  (2022-02-21T10:40:05Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach [50.855679274530615]
  We present a novel domain-adaptive approach called AdaStereo to align multi-level representations for deep stereo matching networks. Our models achieve state-of-the-art cross-domain performance on multiple benchmarks, including KITTI, Middlebury, ETH3D and DrivingStereo. Our method is robust to various domain adaptation settings, and can be easily integrated into quick adaptation application scenarios and real-world deployments.
  arXiv  Detail & Related papers  (2021-12-09T15:10:47Z)
• AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network [8.127449025802436]
  We present a novel recurrent multi-view stereo network based on long short-term memory (LSTM) with adaptive aggregation, namely AA-RMVSNet. We firstly introduce an intra-view aggregation module to adaptively extract image features by using context-aware convolution and multi-scale aggregation. We propose an inter-view cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views.
  arXiv  Detail & Related papers  (2021-08-09T06:10:48Z)

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.