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Sparsh Garg is a Senior Associate Researcher in the Media Analytics Department at NEC Laboratories America. He received his MS in Computer Science and Engineering from Santa Clara University and his BTec in Electronics and Communications Engineering from Manipal Institute of Technology.
At NEC, his research focuses on leveraging LLMs and VLMs to design autonomous AI agents capable of orchestrating and automating the full lifecycle of AI models, from data processing to deployment. He designs efficient and scalable learning algorithms for visual AI, with applications in object detection, person re-identification, video summarization, and perception models for complex, dynamic environments.
His work addresses critical challenges such as adapting to new domains, learning from diverse and often conflicting datasets, and handling rare or previously unseen objects—capabilities essential for ensuring AI reliability in real-world deployments. He develops methods that integrate semantic segmentation, multi-modal learning, and architecture optimization to create models that are not only high-performing but also computationally efficient and interpretable. This balance allows NEC’s visual AI platforms to deliver accurate, explainable results in time-sensitive and safety-critical contexts, from autonomous systems to large-scale video analytics. By combining deep technical innovation with practical deployment considerations, his research strengthens NEC’s ability to deliver AI solutions that operate robustly across varied conditions and evolving operational environments.
Overview: Our AI DevOps pipeline builds a high-fidelity digital twin of sensor data which allows for self-improvement of deployed models. We leverage our foundational vision-language models to automatically determine issues in currently deployed AI, pseudo-label or simulate training data, develop models with continual learning and use an LLM-based verification over diverse scenarios.
Overview: Our techniques from unsupervised and semi-supervised learning, such as domain adaptation and domain generalization, allow robust and responsible AI solutions across multiple applications such as image classification, face recognition, facial anti-spoofing, object detection and semantic segmentation.
Autonomous vehicle (AV) systems rely on robust perception models as a cornerstone of safety assurance. However, objects encountered on the road exhibit a long-tailed distribution, with rare or unseen categories posing challenges to a deployed perception model. This necessitates an expensive process of
While it is desirable to train segmentation models on an aggregation of multiple datasets, a major challenge is that the label space of each dataset may be in conflict with one another. To tackle this challenge, we propose UniSeg, an effective and model-agnostic approach to automatically train segmentation
Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation. We find that, directly applying existing
