35 research outputs found
State-of-the-art Models for Object Detection in Various Fields of Application
We present a list of datasets and their best models with the goal of
advancing the state-of-the-art in object detection by placing the question of
object recognition in the context of the two types of state-of-the-art methods:
one-stage methods and two stage-methods. We provided an in-depth statistical
analysis of the five top datasets in the light of recent developments in
granulated Deep Learning models - COCO minival, COCO test, Pascal VOC 2007,
ADE20K, and ImageNet. The datasets are handpicked after closely comparing them
with the rest in terms of diversity, quality of data, minimal bias, labeling
quality etc. More importantly, our work extends to provide the best combination
of these datasets with the emerging models in the last two years. It lists the
top models and their optimal use cases for each of the respective datasets. We
have provided a comprehensive overview of a variety of both generic and
specific object detection models, enlisting comparative results like inference
time and average precision of box (AP) fixed at different Intersection Over
Union (IoUs) and for different sized objects. The qualitative and quantitative
analysis will allow experts to achieve new performance records using the best
combination of datasets and models.Comment: 4 pages, 5 table
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Deep neural networks currently deliver promising results for microscopy image
cell segmentation, but they require large-scale labelled databases, which is a
costly and time-consuming process. In this work, we relax the labelling
requirement by combining self-supervised with semi-supervised learning. We
propose the prediction of edge-based maps for self-supervising the training of
the unlabelled images, which is combined with the supervised training of a
small number of labelled images for learning the segmentation task. In our
experiments, we evaluate on a few-shot microscopy image cell segmentation
benchmark and show that only a small number of annotated images, e.g. 10% of
the original training set, is enough for our approach to reach similar
performance as with the fully annotated databases on 1- to 10-shots. Our code
and trained models is made publicly availableComment: Accepted by MOVI 202
DigiPig: First Developments of an Automated Monitoring System for Body, Head and Tail Detection in Intensive Pig Farming
publishedVersio
Online Open-set Semi-supervised Object Detection via Semi-supervised Outlier Filtering
Open-set semi-supervised object detection (OSSOD) methods aim to utilize
practical unlabeled datasets with out-of-distribution (OOD) instances for
object detection. The main challenge in OSSOD is distinguishing and filtering
the OOD instances from the in-distribution (ID) instances during
pseudo-labeling. The previous method uses an offline OOD detection network
trained only with labeled data for solving this problem. However, the scarcity
of available data limits the potential for improvement. Meanwhile, training
separately leads to low efficiency. To alleviate the above issues, this paper
proposes a novel end-to-end online framework that improves performance and
efficiency by mining more valuable instances from unlabeled data. Specifically,
we first propose a semi-supervised OOD detection strategy to mine valuable ID
and OOD instances in unlabeled datasets for training. Then, we constitute an
online end-to-end trainable OSSOD framework by integrating the OOD detection
head into the object detector, making it jointly trainable with the original
detection task. Our experimental results show that our method works well on
several benchmarks, including the partially labeled COCO dataset with open-set
classes and the fully labeled COCO dataset with the additional large-scale
open-set unlabeled dataset, OpenImages. Compared with previous OSSOD methods,
our approach achieves the best performance on COCO with OpenImages by +0.94
mAP, reaching 44.07 mAP
Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) aims to improve the performance and
generalization of existing object detectors by utilizing limited labeled data
and extensive unlabeled data. Despite many advances, recent SSOD methods are
still challenged by inadequate model refinement using the classical exponential
moving average (EMA) strategy, the consensus of Teacher-Student models in the
latter stages of training (i.e., losing their distinctiveness), and
noisy/misleading pseudo-labels. This paper proposes a novel training-based
model refinement (TMR) stage and a simple yet effective representation
disagreement (RD) strategy to address the limitations of classical EMA and the
consensus problem. The TMR stage of Teacher-Student models optimizes the
lightweight scaling operation to refine the model's weights and prevent
overfitting or forgetting learned patterns from unlabeled data. Meanwhile, the
RD strategy helps keep these models diverged to encourage the student model to
explore complementary representations. Our approach can be integrated into
established SSOD methods and is empirically validated using two baseline
methods, with and without cascade regression, to generate more reliable
pseudo-labels. Extensive experiments demonstrate the superior performance of
our approach over state-of-the-art SSOD methods. Specifically, the proposed
approach outperforms the baseline Unbiased-Teacher-v2 (& Unbiased-Teacher-v1)
method by an average mAP margin of 2.23, 2.1, and 3.36 (& 2.07, 1.9, and 3.27)
on COCO-standard, COCO-additional, and Pascal VOC datasets, respectively.Comment: Under revie
Adaptive Self-Training for Object Detection
Deep learning has emerged as an effective solution for solving the task of
object detection in images but at the cost of requiring large labeled datasets.
To mitigate this cost, semi-supervised object detection methods, which consist
in leveraging abundant unlabeled data, have been proposed and have already
shown impressive results. However, most of these methods require linking a
pseudo-label to a ground-truth object by thresholding. In previous works, this
threshold value is usually determined empirically, which is time consuming, and
only done for a single data distribution. When the domain, and thus the data
distribution, changes, a new and costly parameter search is necessary. In this
work, we introduce our method Adaptive Self-Training for Object Detection
(ASTOD), which is a simple yet effective teacher-student method. ASTOD
determines without cost a threshold value based directly on the ground value of
the score histogram. To improve the quality of the teacher predictions, we also
propose a novel pseudo-labeling procedure. We use different views of the
unlabeled images during the pseudo-labeling step to reduce the number of missed
predictions and thus obtain better candidate labels. Our teacher and our
student are trained separately, and our method can be used in an iterative
fashion by replacing the teacher by the student. On the MS-COCO dataset, our
method consistently performs favorably against state-of-the-art methods that do
not require a threshold parameter, and shows competitive results with methods
that require a parameter sweep search. Additional experiments with respect to a
supervised baseline on the DIOR dataset containing satellite images lead to
similar conclusions, and prove that it is possible to adapt the score threshold
automatically in self-training, regardless of the data distribution.Comment: 10 pages, 4 figures, 5 table
Semi-Supervised Object Detection in the Open World
Existing approaches for semi-supervised object detection assume a fixed set
of classes present in training and unlabeled datasets, i.e., in-distribution
(ID) data. The performance of these techniques significantly degrades when
these techniques are deployed in the open-world, due to the fact that the
unlabeled and test data may contain objects that were not seen during training,
i.e., out-of-distribution (OOD) data. The two key questions that we explore in
this paper are: can we detect these OOD samples and if so, can we learn from
them? With these considerations in mind, we propose the Open World
Semi-supervised Detection framework (OWSSD) that effectively detects OOD data
along with a semi-supervised learning pipeline that learns from both ID and OOD
data. We introduce an ensemble based OOD detector consisting of lightweight
auto-encoder networks trained only on ID data. Through extensive evalulation,
we demonstrate that our method performs competitively against state-of-the-art
OOD detection algorithms and also significantly boosts the semi-supervised
learning performance in open-world scenarios