Search Results for author: Sinan Kalkan

Found 27 papers, 14 papers with code

A Large-scale Dataset and Benchmark for Similar Trademark Retrieval

2 code implementations20 Jan 2017 Osman Tursun, Cemal Aker, Sinan Kalkan

In this paper, we provide a large-scale dataset with benchmark queries with which different TR approaches can be evaluated systematically.

Retrieval Trademark Retrieval

Using Deep Networks for Drone Detection

no code implementations18 Jun 2017 Cemal Aker, Sinan Kalkan

Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence.

object-detection Object Detection

A Deep Incremental Boltzmann Machine for Modeling Context in Robots

no code implementations13 Oct 2017 Fethiye Irmak Doğan, Hande Çelikkanat, Sinan Kalkan

Context is an essential capability for robots that are to be as adaptive as possible in challenging environments.

General Classification Scene Classification

COSMO: Contextualized Scene Modeling with Boltzmann Machines

1 code implementation2 Jul 2018 Ilker Bozcan, Sinan Kalkan

For this end, we introduce a hybrid version of BMs where relations and affordances are introduced with shared, tri-way connections into the model.

object-detection Object Detection +1

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

3 code implementations ECCV 2018 Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes.

Object object-detection +1

Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments

no code implementations15 Apr 2019 Fethiye Irmak Doğan, Sinan Kalkan, Iolanda Leite

Referring to objects in a natural and unambiguous manner is crucial for effective human-robot interaction.

Referring Expression

Searching for Ambiguous Objects in Videos using Relational Referring Expressions

1 code implementation3 Aug 2019 Hazan Anayurt, Sezai Artun Ozyegin, Ulfet Cetin, Utku Aktas, Sinan Kalkan

Especially in ambiguous settings, humans prefer expressions (called relational referring expressions) that describe an object with respect to a distinguishing, unique object.

Deep Attention Natural Language Visual Grounding +2

Imbalance Problems in Object Detection: A Review

1 code implementation31 Aug 2019 Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas

In this paper, we present a comprehensive review of the imbalance problems in object detection.

Object object-detection +1

Generating Positive Bounding Boxes for Balanced Training of Object Detectors

1 code implementation21 Sep 2019 Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

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.

Object Detection

Investigating Bias and Fairness in Facial Expression Recognition

no code implementations20 Jul 2020 Tian Xu, Jennifer White, Sinan Kalkan, Hatice Gunes

Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding expression labels.

Attribute Data Augmentation +3

Late Temporal Modeling in 3D CNN Architectures with BERT for Action Recognition

1 code implementation3 Aug 2020 M. Esat Kalfaoglu, Sinan Kalkan, A. Aydin Alatan

In this work, we combine 3D convolution with late temporal modeling for action recognition.

A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection

3 code implementations NeurIPS 2020 Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

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.

Classification General Classification +2

Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection

no code implementations13 Nov 2020 Faisal Alamri, Sinan Kalkan, Nicolas Pugeault

Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labeling performance.

Object object-detection +2

Spatio-Temporal Analysis of Facial Actions using Lifecycle-Aware Capsule Networks

no code implementations17 Nov 2020 Nikhil Churamani, Sinan Kalkan, Hatice Gunes

In real-world interactions, however, facial expressions are usually more subtle and evolve in a temporal manner requiring AU detection models to learn spatial as well as temporal information.

One Metric to Measure them All: Localisation Recall Precision (LRP) for Evaluating Visual Detection Tasks

2 code implementations21 Nov 2020 Kemal Oksuz, Baris Can Cam, Sinan Kalkan, Emre Akbas

Despite being widely used as a performance measure for visual detection tasks, Average Precision (AP) is limited in (i) reflecting localisation quality, (ii) interpretability and (iii) robustness to the design choices regarding its computation, and its applicability to outputs without confidence scores.

Instance Segmentation Keypoint Detection +6

Rank & Sort Loss for Object Detection and Instance Segmentation

3 code implementations ICCV 2021 Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan

RS Loss supervises the classifier, a sub-network of these methods, to rank each positive above all negatives as well as to sort positives among themselves with respect to (wrt.)

Instance Segmentation Object +3

Mask-aware IoU for Anchor Assignment in Real-time Instance Segmentation

1 code implementation19 Oct 2021 Kemal Oksuz, Baris Can Cam, Fehmi Kahraman, Zeynep Sonat Baltaci, Sinan Kalkan, Emre Akbas

We present the effectiveness of maIoU on a state-of-the-art (SOTA) assigner, ATSS, by replacing IoU operation by our maIoU and training YOLACT, a SOTA real-time instance segmentation method.

Real-time Instance Segmentation Segmentation +1

Does depth estimation help object detection?

no code implementations13 Apr 2022 Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas

Ground-truth depth, when combined with color data, helps improve object detection accuracy over baseline models that only use color.

Depth Estimation Object +2

Segment Augmentation and Differentiable Ranking for Logo Retrieval

no code implementations6 Sep 2022 Feyza Yavuz, Sinan Kalkan

Logo retrieval is a challenging problem since the definition of similarity is more subjective compared to image retrieval tasks and the set of known similarities is very scarce.

Image Retrieval Retrieval

AssembleRL: Learning to Assemble Furniture from Their Point Clouds

1 code implementation15 Sep 2022 Özgür Aslan, Burak Bolat, Batuhan Bal, Tuğba Tümer, Erol Şahin, Sinan Kalkan

The rise of simulation environments has enabled learning-based approaches for assembly planning, which is otherwise a labor-intensive and daunting task.

Correlation Loss: Enforcing Correlation between Classification and Localization

1 code implementation3 Jan 2023 Fehmi Kahraman, Kemal Oksuz, Sinan Kalkan, Emre Akbas

(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.

Classification Inductive Bias +1

Uncertainty-based Fairness Measures

no code implementations18 Dec 2023 Selim Kuzucu, Jiaee Cheong, Hatice Gunes, Sinan Kalkan

Unfair predictions of machine learning (ML) models impede their broad acceptance in real-world settings.

Fairness

Generalized Mask-aware IoU for Anchor Assignment for Real-time Instance Segmentation

no code implementations28 Dec 2023 Barış Can Çam, Kemal Öksüz, Fehmi Kahraman, Zeynep Sonat Baltaci, Sinan Kalkan, Emre Akbaş

This paper introduces Generalized Mask-aware Intersection-over-Union (GmaIoU) as a new measure for positive-negative assignment of anchor boxes during training of instance segmentation methods.

Real-time Instance Segmentation Segmentation +1

RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses

no code implementations4 Mar 2024 Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas

Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators.

Edge Detection

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