1 code implementation • 18 Nov 2021 • Yantao Lu, Xuetao Hao, Yilan Li, Weiheng Chai, Shiqi Sun, Senem Velipasalar
It is worth to note that our proposed RAA convolution is lightweight and compatible to be integrated into any CNN architecture used for detection from a BEV.
2 code implementations • CVPR 2020 • Yantao Lu, Yunhan Jia, Jian-Yu Wang, Bai Li, Weiheng Chai, Lawrence Carin, Senem Velipasalar
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they remain adversarial even against other models.
1 code implementation • CVPR 2022 • Jiajing Chen, Burak Kakillioglu, Huantao Ren, Senem Velipasalar
In order to address this issue and improve the performance of any baseline 3D point classification or segmentation model, we propose a new module, referred to as the Recycling MaxPooling (RMP) module, to recycle and utilize the features of some of the discarded points.
1 code implementation • 28 Nov 2020 • Fatih Altay, Guillermo Ramon Sanchez, Yanli James, Stephen V. Faraone, Senem Velipasalar, Asif Salekin
Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging.
1 code implementation • 12 Sep 2022 • Feng Wang, M. Cenk Gursoy, Senem Velipasalar
In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches.
1 code implementation • 15 May 2024 • Feng Wang, M. Cenk Gursoy, Senem Velipasalar
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning and federated transfer learning.
1 code implementation • CVPR 2023 • Jiajing Chen, Minmin Yang, Senem Velipasalar
Existing FSL methods for 3D point clouds employ point-based models as their backbone.
Ranked #1 on Few-Shot Point Cloud Classification on ModelNet40
1 code implementation • 16 Jun 2023 • Md Zahid Hasan, Jiajing Chen, Jiyang Wang, Mohammed Shaiqur Rahman, Ameya Joshi, Senem Velipasalar, Chinmay Hegde, Anuj Sharma, Soumik Sarkar
Our results show that this framework offers state-of-the-art performance on zero-shot transfer and video-based CLIP for predicting the driver's state on two public datasets.
1 code implementation • 8 May 2019 • Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.
1 code implementation • 30 Oct 2023 • Feng Wang, Senem Velipasalar, M. Cenk Gursoy
MKOR only requires the server to send secretly modified parameters to clients and can efficiently and inconspicuously reconstruct the input images from clients' gradient updates.
1 code implementation • 25 Jan 2020 • Yilan Li, Senem Velipasalar
Several research work in adversarial machine learning started to focus on the detection of AEs in autonomous driving.
no code implementations • 24 May 2018 • Burak Kakillioglu, Yantao Lu, Senem Velipasalar
Our proposed approach can be used to autonomously refine the parameters, and improve the accuracy of different deep neural network architectures.
no code implementations • 19 Oct 2015 • Mehmet Emin Basbug, Koray Ozcan, Senem Velipasalar
With the advent of smartphones equipped with acceloremeter, gyroscope and camera; it is now possible to develop activity classification platforms everyone can use conveniently.
no code implementations • 24 Sep 2018 • Yantao Lu, Burak Kakillioglu, Senem Velipasalar
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks.
no code implementations • 8 Oct 2018 • Chen Zhong, Ziyang Lu, M. Cenk Gursoy, Senem Velipasalar
We consider the dynamic multichannel access problem, which can be formulated as a partially observable Markov decision process (POMDP).
no code implementations • 16 Oct 2018 • Chuang Ye, M. Cenk Gursoy, Senem Velipasalar
In this paper, wireless video transmission to multiple users under total transmission power and minimum required video quality constraints is studied.
no code implementations • 31 Mar 2019 • Chuang Ye, M. Cenk Gursoy, Senem Velipasalar
Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session.
no code implementations • 13 May 2019 • Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 28 May 2019 • Yantao Lu, Senem Velipasalar
For instance, the sitting activity can be detected by IMU data, but it cannot be determined whether the subject has sat on a chair or a sofa, or where the subject is.
no code implementations • 20 Aug 2019 • Chen Zhong, Ziyang Lu, M. Cenk Gursoy, Senem Velipasalar
We consider both a single-user case and a scenario in which multiple users attempt to access channels simultaneously.
no code implementations • 28 Aug 2019 • Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks.
no code implementations • 12 Jul 2020 • Feng Wang, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
As the applications of deep reinforcement learning (DRL) in wireless communications grow, sensitivity of DRL based wireless communication strategies against adversarial attacks has started to draw increasing attention.
no code implementations • 28 Sep 2020 • Chen Zhong, M. Cenk Gursoy, Senem Velipasalar
In order to improve the detection accuracy and reduce the delay in detection, we introduce a buffer zone in the operation of the proposed GAN-based detector.
no code implementations • 12 May 2021 • Feng Wang, M. Cenk Gursoy, Senem Velipasalar
Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications.
no code implementations • 12 May 2021 • Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney
In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes.
1 code implementation • ICCV 2021 • Chenbin Pan, Senem Velipasalar
Existing variations of CapsNets mainly focus on performance comparison with the original CapsNet, and have not outperformed CNN-based models on complex tasks.
no code implementations • 14 Nov 2021 • Jiajing Chen, Burak Kakillioglu, Senem Velipasalar
As the core module of the DPFA-Net, we propose a Feature Aggregation layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism.
no code implementations • 8 Dec 2021 • Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney
In this setting, we develop an anomaly detection algorithm that chooses the processes to be observed at a given time instant, decides when to stop taking observations, and declares the decision on anomalous processes.
no code implementations • 15 Mar 2023 • Chenbin Pan, Zhiqi Zhang, Senem Velipasalar, Yi Xu
Different from previous video transformers, which use the same static embedding as the class token for diverse inputs, we propose a dynamic class token generator that produces a class token for each input video by analyzing the hand-object interaction and the related motion information.
no code implementations • 1 Apr 2023 • Chenbin Pan, Rui Hou, Hanchao Yu, Qifan Wang, Senem Velipasalar, Madian Khabsa
Whether by processing videos with fixed resolution from start to end or incorporating pooling and down-scaling strategies, existing video transformers process the whole video content throughout the network without specially handling the large portions of redundant information.
no code implementations • CVPR 2023 • Xinglin Li, Jiajing Chen, Jinhui Ouyang, Hanhui Deng, Senem Velipasalar, Di wu
Recent years have witnessed significant developments in point cloud processing, including classification and segmentation.
no code implementations • 19 Nov 2023 • Feng Wang, M. Cenk Gursoy, Senem Velipasalar
We evaluate the performance of the proposed policy ensemble algorithm by applying on the network slicing agents and the jammer agent in simulations to show its effectiveness.
no code implementations • 30 Nov 2023 • Geethu Joseph, Chen Zhong, M. Cenk Gursoy, Senem Velipasalar, Pramod K. Varshney
Our objective is to design a sequential selection policy that dynamically determines which processes to observe at each time with the goal to minimize the delay in making the decision and the total sensing cost.
no code implementations • 10 Jan 2024 • Chenbin Pan, Burhaneddin Yaman, Tommaso Nesti, Abhirup Mallik, Alessandro G Allievi, Senem Velipasalar, Liu Ren
Autonomous driving is a complex and challenging task that aims at safe motion planning through scene understanding and reasoning.
no code implementations • 14 Feb 2024 • Weiheng Chai, Brian Testa, Huantao Ren, Asif Salekin, Senem Velipasalar
The datasets employed are ImageNet, for image classification, Celeba-HQ dataset, for identity classification, and AffectNet, for emotion classification.
no code implementations • 13 Mar 2024 • Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar, Liu Ren
Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation.
no code implementations • 16 Apr 2024 • Huantao Ren, Jiajing Chen, Senem Velipasalar
Our approach models skeleton key points as a 3D point cloud, and employs a computational complexity-conscious 3D point processing approach to extract skeleton features, which are then combined with silhouette features for improved accuracy.
no code implementations • 30 Apr 2024 • Jiyang Wang, Ayse Altay, Senem Velipasalar
To address these issues, we propose an effective method, referred to as the class-aware-block-aware domain adaptation (CABA-DA) which explicitly minimize intra-session variance by viewing different blocks from the same subject same session as different domains.