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Abhishek Aich is a Researcher in the Media Analytics Department at NEC Laboratories America. He received his Bachelor of Technology (B.Tech.) in Electronics and Communications Engineering at Biju Patnaik University of Technology, Odisha, India, Master of Science (M.S.) in Electronics and Communication Engineering at National Institute of Technology, Tiruchirappalli, India and his Ph.D. in 2023 in Electrical and Computer Engineering from the University of California, Riverside, USA. At UC Riverside, he worked with Prof. Amit K. Roy-Chowdhury on topics in computer vision and deep learning.
At NEC Labs, Dr. Aich’s work focuses on vision-language frameworks, perception problems for autonomous driving, dynamic multi-task architectures, efficient transformers, and generative AI. His recent contributions include research on progressive token length scaling for universal segmentation (presented at ICLR 2025, and efficiency–accuracy trade-offs in DETR-style models (CVPRW 2024). He has also helped organize key workshops at ICCV, AAAI, and WACV, and was part of the winning team in the U.S. DOT Intersection Safety Challenge 2025, earning top recognition in Stage 1B.
Overview: While autonomous cars are rapidly becoming a reality, it remains a challenge to scalably deploy them across geographies and conditions. Our full-stack autonomy solutions include perception, prediction, planning, simulation and DevOps that leverage the latest advances in generative AI, neural rendering, large language models, diffusion models and transformers.
Overview: Multi-task learning commonly encounters competition for resources among tasks when model capacity is limited. We develop neural architectures that allow control over the relative importance of tasks and total compute cost during inference time.
Overview: We develop open vocabulary perception methods that combine the power of vision and language to provide rich descriptions of objects in scenes, including their attributes, behaviors, relations and interactions.
A powerful architecture for universal segmentation relies on transformers that encode multi-scale image features and decode object queries into mask predictions. With efficiency being a high priority for scaling such models, we observed that the state-of-the-art method Mask2Former uses >50% of its compute
Our NEC Lab America team attended the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025. The purpose of the AAAI conference series was to promote research in Artificial Intelligence
This report aims to provide a comprehensive view on the inference efficiency of DETR-style detection models. We provide the effect of the basic efficiency techniques and identify the factors that are easily applicable yet effectively improve the efficiency-accuracy trade-off. Specifically, we explore
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on deployed devices. One way to overcome this challenge
We aim to train a multi-task model such that users can adjust the desired compute budget and relative importance of task performances after deployment, without retraining. This enables optimizing performance for dynamically varying user needs, without heavy computational overhead to train and save models
