Adaptive Channel Encoding for Point Cloud Analysis
- URL: http://arxiv.org/abs/2112.02509v1
- Date: Sun, 5 Dec 2021 08:20:27 GMT
- Title: Adaptive Channel Encoding for Point Cloud Analysis
- Authors: Guoquan Xu, Hezhi Cao, Yifan Zhang, Jianwei Wan, Ke Xu, Yanxin Ma
- Abstract summary: An adaptive channel encoding mechanism is proposed to capture channel relationships in this paper.
It improves the quality of the representation generated by the network by explicitly encoding the interdependence between the channels of its features.
- Score: 7.696435157444049
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Attention mechanism plays a more and more important role in point cloud
analysis and channel attention is one of the hotspots. With so much channel
information, it is difficult for neural networks to screen useful channel
information. Thus, an adaptive channel encoding mechanism is proposed to
capture channel relationships in this paper. It improves the quality of the
representation generated by the network by explicitly encoding the
interdependence between the channels of its features. Specifically, a
channel-wise convolution (Channel-Conv) is proposed to adaptively learn the
relationship between coordinates and features, so as to encode the channel.
Different from the popular attention weight schemes, the Channel-Conv proposed
in this paper realizes adaptability in convolution operation, rather than
simply assigning different weights for channels. Extensive experiments on
existing benchmarks verify our method achieves the state of the arts.
Related papers
- RelUNet: Relative Channel Fusion U-Net for Multichannel Speech Enhancement [25.878204820665516]
Multi-channel speech enhancement models, in particular those based on the U-Net architecture, demonstrate promising performance and generalization potential.
We propose a novel modification of these models by incorporating relative information from the outset, where each channel is processed in conjunction with a reference channel through stacking.
This input strategy exploits comparative differences to adaptively fuse information between channels, thereby capturing crucial spatial information and enhancing the overall performance.
arXiv Detail & Related papers (2024-10-07T13:19:10Z) - CSA-Net: Channel-wise Spatially Autocorrelated Attention Networks [19.468704622654357]
We present a channel-wise spatially autocorrelated (CSA) attention mechanism for deep CNNs.
Inspired by geographical analysis, the proposed CSA exploits the spatial relationships between channels of feature maps to produce an effective channel descriptor.
We validate the effectiveness of the proposed CSA networks through extensive experiments and analysis on ImageNet, and MS COCO benchmark datasets.
arXiv Detail & Related papers (2024-05-09T13:21:03Z) - Distributed Deep Joint Source-Channel Coding with Decoder-Only Side
Information [6.411633100057159]
We consider low-latency image transmission over a noisy wireless channel when correlated side information is present only at the receiver side.
We propose a novel neural network architecture that incorporates the decoder-only side information at multiple stages at the receiver side.
arXiv Detail & Related papers (2023-10-06T15:17:45Z) - Joint Channel Estimation and Feedback with Masked Token Transformers in
Massive MIMO Systems [74.52117784544758]
This paper proposes an encoder-decoder based network that unveils the intrinsic frequency-domain correlation within the CSI matrix.
The entire encoder-decoder network is utilized for channel compression.
Our method outperforms state-of-the-art channel estimation and feedback techniques in joint tasks.
arXiv Detail & Related papers (2023-06-08T06:15:17Z) - Attention Based Neural Networks for Wireless Channel Estimation [1.0499453838486013]
We propose a new hybrid encoder-decoder structure (called HA02) for the first time which exploits the attention mechanism to focus on the most important input information.
In particular, we implement a transformer encoder block as the encoder to achieve the sparsity in the input features and a residual neural network as the decoder respectively.
Our simulations show superior estimation performance compared with other candidate neural network methods for channel estimation.
arXiv Detail & Related papers (2022-04-28T12:54:19Z) - Adaptive Channel Encoding Transformer for Point Cloud Analysis [6.90125287791398]
A channel convolution called Transformer-Conv is designed to encode the channel.
It can encode feature channels by capturing the potential relationship between coordinates and features.
Our method is superior to state-of-the-art point cloud classification and segmentation methods on three benchmark datasets.
arXiv Detail & Related papers (2021-12-05T08:18:00Z) - Group Fisher Pruning for Practical Network Compression [58.25776612812883]
We present a general channel pruning approach that can be applied to various complicated structures.
We derive a unified metric based on Fisher information to evaluate the importance of a single channel and coupled channels.
Our method can be used to prune any structures including those with coupled channels.
arXiv Detail & Related papers (2021-08-02T08:21:44Z) - Channel-wise Knowledge Distillation for Dense Prediction [73.99057249472735]
We propose to align features channel-wise between the student and teacher networks.
We consistently achieve superior performance on three benchmarks with various network structures.
arXiv Detail & Related papers (2020-11-26T12:00:38Z) - Channel-wise Alignment for Adaptive Object Detection [66.76486843397267]
Generic object detection has been immensely promoted by the development of deep convolutional neural networks.
Existing methods on this task usually draw attention on the high-level alignment based on the whole image or object of interest.
In this paper, we realize adaptation from a thoroughly different perspective, i.e., channel-wise alignment.
arXiv Detail & Related papers (2020-09-07T02:42:18Z) - Channel-Level Variable Quantization Network for Deep Image Compression [50.3174629451739]
We propose a channel-level variable quantization network to dynamically allocate more convolutions for significant channels and withdraws for negligible channels.
Our method achieves superior performance and can produce much better visual reconstructions.
arXiv Detail & Related papers (2020-07-15T07:20:39Z) - Channel Interaction Networks for Fine-Grained Image Categorization [61.095320862647476]
Fine-grained image categorization is challenging due to the subtle inter-class differences.
We propose a channel interaction network (CIN), which models the channel-wise interplay both within an image and across images.
Our model can be trained efficiently in an end-to-end fashion without the need of multi-stage training and testing.
arXiv Detail & Related papers (2020-03-11T11:51:51Z)
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.