Continual Adaptation for Deep Stereo

• URL: http://arxiv.org/abs/2007.05233v3
• Date: Mon, 3 May 2021 07:53:22 GMT
• Title: Continual Adaptation for Deep Stereo
• Authors: Matteo Poggi, Alessio Tonioni, Fabio Tosi, Stefano Mattoccia, Luigi Di Stefano
• Abstract summary: We propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. Our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system.
• Score: 52.181067640300014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth estimation from stereo images is carried out with unmatched results by convolutional neural networks trained end-to-end to regress dense disparities. Like for most tasks, this is possible if large amounts of labelled samples are available for training, possibly covering the whole data distribution encountered at deployment time. Being such an assumption systematically unmet in real applications, the capacity of adapting to any unseen setting becomes of paramount importance. Purposely, we propose a continual adaptation paradigm for deep stereo networks designed to deal with challenging and ever-changing environments. We design a lightweight and modular architecture, Modularly ADaptive Network (MADNet), and formulate Modular ADaptation algorithms (MAD, MAD++) which permit efficient optimization of independent sub-portions of the entire network. In our paradigm, the learning signals needed to continuously adapt models online can be sourced from self-supervision via right-to-left image warping or from traditional stereo algorithms. With both sources, no other data than the input images being gathered at deployment time are needed. Thus, our network architecture and adaptation algorithms realize the first real-time self-adaptive deep stereo system and pave the way for a new paradigm that can facilitate practical deployment of end-to-end architectures for dense disparity regression.

Related papers

• Double-Shot 3D Shape Measurement with a Dual-Branch Network [14.749887303860717]
  We propose a dual-branch Convolutional Neural Network (CNN)-Transformer network (PDCNet) to process different structured light (SL) modalities. Within PDCNet, a Transformer branch is used to capture global perception in the fringe images, while a CNN branch is designed to collect local details in the speckle images. We show that our method can reduce fringe order ambiguity while producing high-accuracy results on a self-made dataset.
  arXiv  Detail & Related papers  (2024-07-19T10:49:26Z)
• PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation [67.41325356479229]
  We propose to incorporate an auxiliary point-selective network into a meta-learning framework, called PointFix. In a nutshell, our auxiliary network learns to fix local variants intensively by effectively back-propagating local information through the meta-gradient. This network is model-agnostic, so can be used in any kind of architectures in a plug-and-play manner.
  arXiv  Detail & Related papers  (2022-07-27T07:48:29Z)
• Engineering flexible machine learning systems by traversing functionally-invariant paths [1.4999444543328289]
  We introduce a differential geometry framework that provides flexible and continuous adaptation of neural networks. We formalize adaptation as movement along a geodesic path in weight space while searching for networks that accommodate secondary objectives. With modest computational resources, the FIP algorithm achieves comparable to state of the art performance on continual learning and sparsification tasks.
  arXiv  Detail & Related papers  (2022-04-30T19:44:56Z)
• 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)
• Shape Adaptor: A Learnable Resizing Module [59.940372879848624]
  We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers. Our implementation enables shape adaptors to be trained end-to-end without any additional supervision. We show the effectiveness of shape adaptors on two other applications: network compression and transfer learning.
  arXiv  Detail & Related papers  (2020-08-03T14:15:52Z)
• DANR: Discrepancy-aware Network Regularization [15.239252118069762]
  Network regularization is an effective tool for learning coherent models over networks. We propose a novel approach that is robust to inadequate regularizations and effectively captures model evolution and structural changes over-temporal networks. We develop a scalable and scalable algorithm based on the alternating method of multipliers (ADMM) to solve the proposed problem with guaranteed convergence to global optimum solutions.
  arXiv  Detail & Related papers  (2020-05-31T02:01:19Z)
• Deep Adaptive Inference Networks for Single Image Super-Resolution [72.7304455761067]
  Single image super-resolution (SISR) has witnessed tremendous progress in recent years owing to the deployment of deep convolutional neural networks (CNNs) In this paper, we take a step forward to address this issue by leveraging the adaptive inference networks for deep SISR (AdaDSR) Our AdaDSR involves an SISR model as backbone and a lightweight adapter module which takes image features and resource constraint as input and predicts a map of local network depth.
  arXiv  Detail & Related papers  (2020-04-08T10:08:20Z)
• Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment [84.57874289554839]
  Training deep neural networks on large-scale datasets requires significant hardware resources. Backpropagation, the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. We propose a neuro-biologically-plausible alternative to backprop that can be used to train deep networks.
  arXiv  Detail & Related papers  (2020-02-10T16:20:02Z)

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.