Computer Vision
Modern computer vision systems have superhuman accuracy when it comes to image recognition and analysis, but they don’t really understand what they see. At IBM Research, we’re designing AI systems with the ability to see the world like we do.
Our work
Environmental analysis made easier with IBM’s Geospatial Studio
NewsKim MartineauWhat is prompt-tuning?
NewsKim MartineauIBM and NASA team up to spur new discoveries about our planet
NewsKim Martineau3 minute readDebugging foundation models for bias
ResearchKim MartineauNew smartphone app to navigate blind people to stand in lines with distances
ResearchHironobu Takagi, Chieko Asakawa, Masaki Kuribayashi, and Seita Kayukawa3 minute readMIT and IBM announce ThreeDWorld Transport Challenge for physically realistic Embodied AI
ReleaseChuang Gan, Abhishek Bhandwaldar, and Dan Gutfreund7 minute read- See more of our work on Computer Vision
Publications
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging
- Ismail Erbas
- Vikas Pandey
- et al.
- 2024
- NeurIPS 2024
Kernel approximation using analogue in-memory computing
- Julian Büchel
- Giacomo Camposampiero
- et al.
- 2024
- Nature Machine Intelligence
Modern Hopfield Networks meet Encoded Neural Representations - Addressing Practical Considerations
- 2024
- NeurIPS 2024
MS-CLIP: Multi-spectral Vision Language Learning for Earth Observation
- 2024
- AGU 2024
Global Area Sampling for Geospatial Foundation Model
- 2024
- AGU 2024
Predicting NDVI from SAR images toward Above Ground Biomass Estimation
- Daiki Kimura
- Tatsuya Ishikawa
- et al.
- 2024
- AGU 2024