17 citations · 29 across the 11 of their papers we have counts for
13 papers
Privacy-enhancing Sclera Segmentation Benchmarking Competition: SSBC 2025
Matej Vitek, Darian Tomašević, Abhijit Das +32
This paper presents a summary of the 2025 Sclera Segmentation Benchmarking Competition (SSBC), which focused on the development of privacy-preserving sclera-segmentation models tra…
Compact Recurrent Transformer with Persistent Memory
Edison Mucllari, Zachary Daniels, David Zhang +1
The Transformer architecture has shown significant success in many language processing and visual tasks. However, the method faces challenges in efficiently scaling to long sequenc…
Noise-Tolerant Coreset-Based Class Incremental Continual Learning
Edison Mucllari, Aswin Raghavan, Zachary Alan Daniels
Many applications of computer vision require the ability to adapt to novel data distributions after deployment. Adaptation requires algorithms capable of continual learning (CL). C…
Data-Driven Pixel Control: Challenges and Prospects
Saurabh Farkya, Zachary Alan Daniels, Aswin Raghavan +4
Recent advancements in sensors have led to high resolution and high data throughput at the pixel level. Simultaneously, the adoption of increasingly large (deep) neural networks (N…
Efficient Model Adaptation for Continual Learning at the Edge
Zachary A. Daniels, Jun Hu, Michael Lomnitz +5
Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed o…
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
Indranil Sur, Zachary Daniels, Abrar Rahman +16
As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adap…
Learning with Local Gradients at the Edge
Michael Lomnitz, Zachary Daniels, David Zhang +1
To enable learning on edge devices with fast convergence and low memory, we present a novel backpropagation-free optimization algorithm dubbed Target Projection Stochastic Gradient…
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
Zachary Daniels, Aswin Raghavan, Jesse Hostetler +4
One approach to meet the challenges of deep lifelong reinforcement learning (LRL) is careful management of the agent's learning experiences, to learn (without forgetting) and build…
A Dynamic Data Driven Approach for Explainable Scene Understanding
Zachary A Daniels, Dimitris Metaxas
Scene-understanding is an important topic in the area of Computer Vision, and illustrates computational challenges with applications to a wide range of domains including remote sen…
Real-time Hyper-Dimensional Reconfiguration at the Edge using Hardware Accelerators
Indhumathi Kandaswamy, Saurabh Farkya, Zachary Daniels +8
In this paper we present Hyper-Dimensional Reconfigurable Analytics at the Tactical Edge (HyDRATE) using low-SWaP embedded hardware that can perform real-time reconfiguration at th…
Saccade Mechanisms for Image Classification, Object Detection and Tracking
Saurabh Farkya, Zachary Daniels, Aswin Nadamuni Raghavan +2
We examine how the saccade mechanism from biological vision can be used to make deep neural networks more efficient for classification and object detection problems. Our proposed a…
Scenarios: A New Representation for Complex Scene Understanding
Zachary A. Daniels, Dimitris N. Metaxas
The ability for computational agents to reason about the high-level content of real world scene images is important for many applications. Existing attempts at addressing the probl…