Scholar
Samarth Mishra
Google Scholar ID: Vxk4TM4AAAAJ
Waymo
Citations & Impact
Citations
300
H-index
8
i10-index
8
Publications
18
Co-authors
28
list available
Publications
Resume (English only)
Academic Achievements
- [{'PaperTitle': 'SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models', 'Conference': 'CVPR 2025'}, {'PaperTitle': 'Learning Human Action Recognition Representations Without Real Humans', 'Conference': 'NeurIPS 2023 D&B'}, {'PaperTitle': 'How Transferable are Video Representations Based on Synthetic Data?', 'Conference': 'NeurIPS 2022 D&B'}, {'PaperTitle': 'Task2Sim: Towards effective pre-training and transfer from synthetic data', 'Conference': 'CVPR 2022'}, {'PaperTitle': 'Visda-2021 competition : Universal domain adaptation to improve performance on out-of-distribution data', 'Conference': 'NeurIPS 2021 Competitions Track'}, {'PaperTitle': 'Surprisingly Simple Semi-Supervised Domain Adaptation with Pretraining and Consistency', 'Conference': 'BMVC 2021'}, {'PaperTitle': 'Effectively leveraging attributes for visual similarity', 'Conference': 'ICCV 2021'}, {'PaperTitle': 'Incremental Object Learning from Contiguous Views', 'Conference': 'Oral, Best paper finalist CVPR 2019'}, {'PaperTitle': 'Faster Algorithms for Weighted Recursive State Machines', 'Conference': 'ESOP 2017'}, {'PaperTitle': 'Learning Compositional Representations for Effective Low-Shot Generalization', 'Journal': 'IEEE Transactions on Pattern Analysis and Machine Intelligence'}, {'PaperTitle': 'SCRAMBLe : Enhancing Multimodal LLM Compositionality with Synthetic Preference Data', 'Conference': 'ICCV 2025 Findings'}, {'PaperTitle': 'Self-supervised visual attribute learning for fashion compatibility', 'Conference': 'ICCV 2021 VIPriors Workshop'}, {'PaperTitle': 'Low dimensional visual attributes: An interpretable image encoding', 'Conference': 'ICPR 2020 EDL/AI Workshop'}, {'PaperTitle': 'SynCDR : Training Cross Domain Retrieval Models with Synthetic Data', 'Preprint': 'arXiv'}]
Research Experience
- Software Engineer, Perception @ Waymo
Background
- Research Interests: Computer Vision, Machine Learning, Large Multimodal Models, Synthetic Data.
Miscellany
- Relevant Coursework:
- - Boston University: Towards Universal Natural Language Understanding, Reinforcement Learning, Statistical Learning Theory
- - Georgia Tech: Machine Learning, Numerical Linear Algebra, Machine Learning Theory
- - IIT Bombay: Advanced Machine Learning (Probabilistic Graphical Models and Deep Learning), Foundations of Intelligent and Learning Agents, Algorithms in Medical Image Processing, Digital Image Processing
