Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics

• URL: http://arxiv.org/abs/2012.14345v1
• Date: Mon, 28 Dec 2020 16:36:11 GMT
• Title: Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics
• Authors: Elisa Maiettini and Raffaello Camoriano and Giulia Pasquale and Vadim Tikhanoff and Lorenzo Rosasco and Lorenzo Natale
• Abstract summary: Several object detection methods have been proposed in the literature, the vast majority based on Deep Convolutional Neural Networks (DCNNs) These methods have important limitations for robotics: Learning solely on off-line data may introduce biases, and prevents adaptation to novel tasks. In this work, we investigate how weakly-supervised learning can cope with these problems.
• Score: 24.878465999976594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several object detection methods have recently been proposed in the literature, the vast majority based on Deep Convolutional Neural Networks (DCNNs). Such architectures have been shown to achieve remarkable performance, at the cost of computationally expensive batch training and extensive labeling. These methods have important limitations for robotics: Learning solely on off-line data may introduce biases (the so-called domain shift), and prevents adaptation to novel tasks. In this work, we investigate how weakly-supervised learning can cope with these problems. We compare several techniques for weakly-supervised learning in detection pipelines to reduce model (re)training costs without compromising accuracy. In particular, we show that diversity sampling for constructing active learning queries and strong positives selection for self-supervised learning enable significant annotation savings and improve domain shift adaptation. By integrating our strategies into a hybrid DCNN/FALKON on-line detection pipeline [1], our method is able to be trained and updated efficiently with few labels, overcoming limitations of previous work. We experimentally validate and benchmark our method on challenging robotic object detection tasks under domain shift.

Related papers

• Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer [12.042768320132694]
  This paper presents a review of 27 cutting-edge developments in semi-supervised learning for object detection. It covers data augmentation techniques, pseudo-labeling strategies, consistency regularization, and adversarial training methods. We aim to ignite further research interest in overcoming existing challenges and exploring new directions in semi-supervised learning for object detection.
  arXiv  Detail & Related papers  (2024-07-11T12:58:13Z)
• Efficient Deep Reinforcement Learning Requires Regulating Overfitting [91.88004732618381]
  We show that high temporal-difference (TD) error on the validation set of transitions is the main culprit that severely affects the performance of deep RL algorithms. We show that a simple online model selection method that targets the validation TD error is effective across state-based DMC and Gym tasks.
  arXiv  Detail & Related papers  (2023-04-20T17:11:05Z)
• Label-Efficient Object Detection via Region Proposal Network Pre-Training [58.50615557874024]
  We propose a simple pretext task that provides an effective pre-training for the region proposal network (RPN) In comparison with multi-stage detectors without RPN pre-training, our approach is able to consistently improve downstream task performance.
  arXiv  Detail & Related papers  (2022-11-16T16:28:18Z)
• What Matters in Learning from Offline Human Demonstrations for Robot Manipulation [64.43440450794495]
  We conduct an extensive study of six offline learning algorithms for robot manipulation. Our study analyzes the most critical challenges when learning from offline human data. We highlight opportunities for learning from human datasets.
  arXiv  Detail & Related papers  (2021-08-06T20:48:30Z)
• Improving Botnet Detection with Recurrent Neural Network and Transfer Learning [5.602292536933117]
  Botnet detection is a critical step in stopping the spread of botnets and preventing malicious activities. Recent approaches employing machine learning (ML) showed improved performance than earlier ones. We propose a novel botnet detection method, built upon Recurrent Variational Autoencoder (RVAE)
  arXiv  Detail & Related papers  (2021-04-26T14:05:01Z)
• Few-Cost Salient Object Detection with Adversarial-Paced Learning [95.0220555274653]
  This paper proposes to learn the effective salient object detection model based on the manual annotation on a few training images only. We name this task as the few-cost salient object detection and propose an adversarial-paced learning (APL)-based framework to facilitate the few-cost learning scenario.
  arXiv  Detail & Related papers  (2021-04-05T14:15:49Z)
• SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation [16.281712605385316]
  Incremental learning requires a model to continually learn new tasks from streaming data. Traditional fine-tuning of a well-trained deep neural network on a new task will dramatically degrade performance on the old task. We propose a novel incremental learning paradigm called Selective and Inter-related Distillation (SID)
  arXiv  Detail & Related papers  (2020-12-31T04:12:06Z)
• Bayesian active learning for production, a systematic study and a reusable library [85.32971950095742]
  In this paper, we analyse the main drawbacks of current active learning techniques. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size.
  arXiv  Detail & Related papers  (2020-06-17T14:51:11Z)
• Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks [27.44348371795822]
  We develop a statistical minimax framework to characterize the limits of transfer learning. We derive a lower-bound for the target generalization error achievable by any algorithm as a function of the number of labeled source and target data.
  arXiv  Detail & Related papers  (2020-06-16T22:49:26Z)
• Scalable Active Learning for Object Detection [20.99502312184771]
  Active learning is a powerful technique to improve data efficiency for supervised learning methods. We have built a scalable production system for active learning in the domain of autonomous driving.
  arXiv  Detail & Related papers  (2020-04-09T17:28:56Z)
• Incremental Object Detection via Meta-Learning [77.55310507917012]
  We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
  arXiv  Detail & Related papers  (2020-03-17T13:40:00Z)

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.