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20222026
most citedLearned Scalable Video Coding For Humans and Machines

10 citations · 11 across the 11 of their papers we have counts for

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Showing 2023Show all

8 papers · 1 filter

eess.IV2023

Metaverse: A Young Gamer's Perspective

Ivan V. Bajić, Teo Saeedi-Bajić, Kai Saeedi-Bajić

When developing technologies for the Metaverse, it is important to understand the needs and requirements of end users. Relatively little is known about the specific perspectives on…

eess.IV2023★ 1 cited

Base Layer Efficiency in Scalable Human-Machine Coding

Yalda Foroutan, Alon Harell, Anderson de Andrade +1

A basic premise in scalable human-machine coding is that the base layer is intended for automated machine analysis and is therefore more compressible than the same content would be…

eess.IV2023

Grad-FEC: Unequal Loss Protection of Deep Features in Collaborative Intelligence

Korcan Uyanik, S. Faegheh Yeganli, Ivan V. Bajić

Collaborative intelligence (CI) involves dividing an artificial intelligence (AI) model into two parts: front-end, to be deployed on an edge device, and back-end, to be deployed in…

eess.IV2023★ 10 cited

Learned Scalable Video Coding For Humans and Machines

Hadi Hadizadeh, Ivan V. Bajić

Video coding has traditionally been developed to support services such as video streaming, videoconferencing, digital TV, and so on. The main intent was to enable human viewing of…

cs.CV2023

Adversarial Attacks and Defenses on 3D Point Cloud Classification: A Survey

Hanieh Naderi, Ivan V. Bajić

Deep learning has successfully solved a wide range of tasks in 2D vision as a dominant AI technique. Recently, deep learning on 3D point clouds is becoming increasingly popular for…

eess.IV2023

Conditional and Residual Methods in Scalable Coding for Humans and Machines

Anderson de Andrade, Alon Harell, Yalda Foroutan +1

We present methods for conditional and residual coding in the context of scalable coding for humans and machines. Our focus is on optimizing the rate-distortion performance of the…

cs.CV2023

Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations

Zahra Hafezi Kafshgari, Chamani Shiranthika, Parvaneh Saeedi +1

SplitFed Learning, a combination of Federated and Split Learning (FL and SL), is one of the most recent developments in the decentralized machine learning domain. In SplitFed learn…

eess.IV2023

Multi-Task Learning for Screen Content Image Coding

Rashid Zamanshoar Heris, Ivan V. Bajić

With the rise of remote work and collaboration, compression of screen content images (SCI) is becoming increasingly important. While there are efficient codecs for natural images,…