12 research outputs found
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
We propose average Localisation-Recall-Precision (aLRP), a unified, bounded,
balanced and ranking-based loss function for both classification and
localisation tasks in object detection. aLRP extends the
Localisation-Recall-Precision (LRP) performance metric (Oksuz et al., 2018)
inspired from how Average Precision (AP) Loss extends precision to a
ranking-based loss function for classification (Chen et al., 2020). aLRP has
the following distinct advantages: (i) aLRP is the first ranking-based loss
function for both classification and localisation tasks. (ii) Thanks to using
ranking for both tasks, aLRP naturally enforces high-quality localisation for
high-precision classification. (iii) aLRP provides provable balance between
positives and negatives. (iv) Compared to on average 6 hyperparameters in
the loss functions of state-of-the-art detectors, aLRP Loss has only one
hyperparameter, which we did not tune in practice. On the COCO dataset, aLRP
Loss improves its ranking-based predecessor, AP Loss, up to around AP
points, achieves AP without test time augmentation and outperforms all
one-stage detectors. Code available at: https://github.com/kemaloksuz/aLRPLoss .Comment: NeurIPS 2020 spotlight pape
Generating Positive Bounding Boxes for Balanced Training of Object Detectors
Two-stage deep object detectors generate a set of regions-of-interest (RoI)
in the first stage, then, in the second stage, identify objects among the
proposed RoIs that sufficiently overlap with a ground truth (GT) box. The
second stage is known to suffer from a bias towards RoIs that have low
intersection-over-union (IoU) with the associated GT boxes. To address this
issue, we first propose a sampling method to generate bounding boxes (BB) that
overlap with a given reference box more than a given IoU threshold. Then, we
use this BB generation method to develop a positive RoI (pRoI) generator that
produces RoIs following any desired spatial or IoU distribution, for the
second-stage. We show that our pRoI generator is able to simulate other
sampling methods for positive examples such as hard example mining and prime
sampling. Using our generator as an analysis tool, we show that (i) IoU
imbalance has an adverse effect on performance, (ii) hard positive example
mining improves the performance only for certain input IoU distributions, and
(iii) the imbalance among the foreground classes has an adverse effect on
performance and that it can be alleviated at the batch level. Finally, we train
Faster R-CNN using our pRoI generator and, compared to conventional training,
obtain better or on-par performance for low IoUs and significant improvements
when trained for higher IoUs for Pascal VOC and MS COCO datasets. The code is
available at: https://github.com/kemaloksuz/BoundingBoxGenerator.Comment: To appear in WACV 2
Imbalance Problems in Object Detection: A Review
In this paper, we present a comprehensive review of the imbalance problems in
object detection. To analyze the problems in a systematic manner, we introduce
a problem-based taxonomy. Following this taxonomy, we discuss each problem in
depth and present a unifying yet critical perspective on the solutions in the
literature. In addition, we identify major open issues regarding the existing
imbalance problems as well as imbalance problems that have not been discussed
before. Moreover, in order to keep our review up to date, we provide an
accompanying webpage which catalogs papers addressing imbalance problems,
according to our problem-based taxonomy. Researchers can track newer studies on
this webpage available at:
https://github.com/kemaloksuz/ObjectDetectionImbalance .Comment: Accepted to IEEE TPAMI; currently in pres
EPIdemiology of Surgery-Associated Acute Kidney Injury (EPIS-AKI) : Study protocol for a multicentre, observational trial
More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369
Correlation Loss: Enforcing Correlation between Classification and Localization
Object detectors are conventionally trained by a weighted sum of classification and localization losses. Recent studies (e.g., predicting IoU with an auxiliary head, Generalized Focal Loss, Rank & Sort Loss) have shown that forcing these two loss terms to interact with each other in non-conventional ways creates a useful inductive bias and improves performance. Inspired by these works, we focus on the correlation between classification and localization and make two main contributions: (i) We provide an analysis about the effects of correlation between classification and localization tasks in object detectors. We identify why correlation affects the performance of various NMS-based and NMS-free detectors, and we devise measures to evaluate the effect of correlation and use them to analyze common detectors. (ii) Motivated by our observations, e.g., that NMS-free detectors can also benefit from correlation, we propose Correlation Loss, a novel plug-in loss function that improves the performance of various object detectors by directly optimizing correlation coefficients: E.g., Correlation Loss on Sparse R-CNN, an NMS-free method, yields 1.6 AP gain on COCO and 1.8 AP gain on Cityscapes dataset. Our best model on Sparse R-CNN reaches 51.0 AP without test-time augmentation on COCO test-dev, reaching state-of-the-art. Code is available at: https://github.com/fehmikahraman/CorrLoss
Linear and nonlinear site response analyses to determine dynamic soil properties of Kirikkale
In order to make reliable earthquake-resistant design of civil
engineering structures, one of the most important considerations in a
region with high seismicity is to pay attention to the local soil
condition of regions. It is aimed in the current study at specifying
dynamic soil characteristics of Kirikkale city center conducting the 1-D
equivalent linear and non-linear site response analyses. Due to high
vulnerability and seismicity of the city center of Kirikkale surrounded
by active many faults, such as the North Anatolian Fault (NAF), the city
of Kirikkale is classified as highly earthquake-prone city. The first
effort to determine critical site response parameter is to perform the
seismic hazard analyses of the region through the earthquake record
catalogues. The moment magnitude of the city center is obtained as
M-W,=7.0 according to the recorded probability of exceedance of 10\% in
the last 50 years. Using the data from site tests, the 1-D equivalent
linear (EL) and nonlinear site response analyses (NL) are performed with
respect to the shear modulus reduction and damping ratio models proposed
in literature. The important engineering parameters of the amplification
ratio, predominant site period, peak ground acceleration (PGA) and
spectral acceleration values are predicted. Except for the periods
between the period of T=12-1.0 s, the results from the NL are obtained
to be similar to the EL results. Lower spectral acceleration values are
estimated in the locations of the city where the higher amplification
ratio is attained or vice-versa. Construction of high-rise buildings
with modal periods higher than T=1.0 s are obtained to be suitable for
the city of Kirikkale. The buildings at the city center are recommended
to be assessed with street survey rapid structural evaluation methods so
as to mitigate seismic damages. The obtained contour maps in this study
are estimated to be effective for visually characterizing the city in
terms of the considered parameters
Class Uncertainty: A Measure to Mitigate Class Imbalance
Class-wise characteristics of training examples affect the performance of deep classifiers. A well-studied example is when the number of training examples of classes follows a long-tailed distribution, a situation that is likely to yield suboptimal performance for under-represented classes. This class imbalance problem is conventionally addressed by approaches relying on the class-wise cardinality of training examples, such as data resampling. In this paper, we demonstrate that considering solely the cardinality of classes does not cover all issues causing class imbalance. To measure class imbalance, we propose CLASS UNCERTAINTY as the average predictive uncertainty of the training examples, and we show that this novel measure captures the differences across classes better than cardinality. We also curate SVCI-20 as a novel dataset in which the classes have equal number of training examples but they differ in terms of their hardness; thereby causing a type of class imbalance which cannot be addressed by the approaches relying on cardinality. We incorporate our CLASS UNCERTAINTY measure into a diverse set of ten class imbalance mitigation methods to demonstrate its effectiveness on long-tailed datasets as well as on our SVCI-20. Code and datasets will be made available