GAIA: A Transfer Learning System of Object Detection that Fits Your Needs

• URL: http://arxiv.org/abs/2106.11346v1
• Date: Mon, 21 Jun 2021 18:24:20 GMT
• Title: GAIA: A Transfer Learning System of Object Detection that Fits Your Needs
• Authors: Xingyuan Bu, Junran Peng, Junjie Yan, Tieniu Tan, Zhaoxiang Zhang
• Abstract summary: Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains.
• Score: 136.60609274344893
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA, which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains, and collecting relevant data for practitioners who have very few datapoints for their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, Caltech, CityPersons, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an example, GAIA is able to efficiently produce models covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells. To benefit every practitioner in the community of object detection, GAIA is released at https://github.com/GAIA-vision.

Related papers

• Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks [62.12107686529827]
  This article highlights a significant shift towards leveraging quantum computing techniques in processing large volumes of remote sensing data. The proposed Quanv4EO model introduces a quanvolution method for preprocessing multi-dimensional EO data. Key findings suggest that the proposed model not only maintains high precision in image classification but also shows improvements of around 5% in EO use cases.
  arXiv  Detail & Related papers  (2024-07-24T09:11:34Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• Efficient Data Distribution Estimation for Accelerated Federated Learning [5.085889377571319]
  Federated Learning(FL) is a privacy-preserving machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. Devices are highly heterogeneous in both their system resources and training data. Various client selection algorithms have been developed, showing promising performance improvement in terms of model coverage and accuracy.
  arXiv  Detail & Related papers  (2024-06-03T20:33:17Z)
• LESS: Selecting Influential Data for Targeted Instruction Tuning [64.78894228923619]
  We propose LESS, an efficient algorithm to estimate data influences and perform Low-rank gradiEnt Similarity Search for instruction data selection. We show that training on a LESS-selected 5% of the data can often outperform training on the full dataset across diverse downstream tasks. Our method goes beyond surface form cues to identify data that the necessary reasoning skills for the intended downstream application.
  arXiv  Detail & Related papers  (2024-02-06T19:18:04Z)
• Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity [1.274578243851308]
  We propose a novel strategy to combine publicly available datasets with the goal of learning a generalized HAR model. Our experimental evaluation, which includes experimenting with different state-of-the-art neural network architectures, shows that combining public datasets can significantly reduce the number of labeled samples.
  arXiv  Detail & Related papers  (2023-06-23T18:51:22Z)
• ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme [37.099990745974196]
  We introduce an On-Demand-FL, a client deployment approach for federated learning. We make use of containerization technology such as Docker to build efficient environments. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem.
  arXiv  Detail & Related papers  (2022-11-05T13:41:19Z)
• DATA: Domain-Aware and Task-Aware Pre-training [94.62676913928831]
  We present DATA, a simple yet effective NAS approach specialized for self-supervised learning (SSL) Our method achieves promising results across a wide range of computation costs on downstream tasks, including image classification, object detection and semantic segmentation.
  arXiv  Detail & Related papers  (2022-03-17T02:38:49Z)
• Gram-SLD: Automatic Self-labeling and Detection for Instance Objects [6.512856940779818]
  We propose a new framework based on co-training called Gram Self-Labeling and Detection (Gram-SLD) Gram-SLD can automatically annotate a large amount of data with very limited manually labeled key data and achieve competitive performance.
  arXiv  Detail & Related papers  (2021-12-07T11:34:55Z)
• GI-NNet \& RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigms [0.0]
  We use deep learning techniques to help robots learn to generate and execute appropriate grasps quickly. We developed a Generative Inception Neural Network (GI-NNet) model, capable of generating antipodal robotic grasps on seen as well as unseen objects.
  arXiv  Detail & Related papers  (2021-07-15T16:55:49Z)
• 2nd Place Scheme on Action Recognition Track of ECCV 2020 VIPriors Challenges: An Efficient Optical Flow Stream Guided Framework [57.847010327319964]
  We propose a data-efficient framework that can train the model from scratch on small datasets. Specifically, by introducing a 3D central difference convolution operation, we proposed a novel C3D neural network-based two-stream framework. It is proved that our method can achieve a promising result even without a pre-trained model on large scale datasets.
  arXiv  Detail & Related papers  (2020-08-10T09:50:28Z)

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.