Mehdi S. M. Sajjadi
Research Scientist Tech Lead & Manager Google DeepMind
Github contact[at]msajjadi.com
Publications (Google Scholar)
- 🔥 DyST: Towards Dynamic Neural Scene Representations on Real-World Videos (ICLR 2024 Spotlight, top 5%)
- 🔥 RUST: Latent Neural Scene Representations from Unposed Imagery (CVPR 2023 Highlight, top 2.5%)
- PaLM-E: An Embodied Multimodal Language Model (ICML 2023)
- DORSal: Diffusion for Object-centric Representations of Scenes et al. (ICLR 2024)
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Sensitivity of Slot-Based Object-Centric Models to their Number of Slots
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RePAST: Relative Pose Attention Scene Representation Transformer (CVPR 2023 Workshops 3DMV & T4V Spotlight)
- Test-time adaptation with slot-centric models (ICML 2023, NeurIPS 2022 Workshops MetaLearn & DistShift)
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Spatial Symmetry in Slot Attention (ICML 2023, NeurIPS 2022 Workshop NeurReps)
- Object Scene Representation Transformer (NeurIPS 2022)
- 🔥 Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations (CVPR 2022)
- RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs (CVPR 2022 Oral)
- Kubric (CVPR 2022)
- NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes (TMLR 2022)
- NeRF in the Wild (CVPR 2021 Oral)
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From Variational to Deterministic Autoencoders (ICLR 2020)
- Assessing Generative Models via Precision and Recall (NeurIPS 2018 & ICML 2018 workshop TADGM Oral & Best Poster Award at Bosch AI Con 2018)
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Photorealistic Video Super-Resolution with Enhanced Temporal Consistency (ECCV 2018 workshop PIRM)
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Spatio-temporal Transformer Network for Video Restoration (ECCV 2018)
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Tempered Adversarial Networks (ICML 2018 Oral & ICLR 2018 workshop)
- Frame-Recurrent Video Super-Resolution (CVPR 2018)
- EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (ICCV 2017 Oral, top 2%)
- Project website
- GitHub
- Our work has been covered in the press:
- Depth Estimation Through a Generative Model of Light Field Synthesis (GCPR 2016)
- Peer Grading in a Course on Algorithms and Data Structures: Machine Learning Algorithms do not Improve over Simple Baselines (L@S 2016 Oral & ICML 2015 workshops CrowdML & ML4Ed)