Why fuse data?
Each data type brings a different piece of information; together, they provide a more complete view. This is precisely the goal of multimodal fusion: to achieve more refined analyses, more robust in challenging conditions.
The result: greater accuracy and better support for decision-making.
Examples of common modalities:
- Acoustic imaging (ultrasound, vibroacoustics, etc.)
- Hyperspectral imaging (Infrared, X-ray, RGB)
- Mapping data (topography, hydrology, cadastral, etc.)
Keeping only the useful info, at the right time (gating)
What is gating?
- It is a mechanism specific to certain neural networks that controls the flow of information within the model.
- “Gates” learn to open or close access to certain data streams, keeping only the information relevant to the analysis, at every moment.
What is it used for?
- On time-based data (video, sensor series), it keeps the useful context and reduces noise.
- In fusion, the combination becomes adaptive : each data type contributes at the right time depending on its relevance.
- Result : more stable outputs and more consistent results.
Typical use cases in multimodal fusion
Covered domains
- Quality control & predictive maintenance
- Non-destructive testing
- Mobility / Automotive
- Diagnostic support
Key benefits
- Robustness against variations (lighting, noise, occlusions)
- Reduced ambiguity thanks to data complementarity
- A common foundation that eases adaptation to new contexts
Our fusion architecture
We have designed a general-purpose neural network architecture for rasterized datasets (2D grid data) and associated signals. It quickly adapts to new contexts without heavy R&D investment.
In practice, it integrates :
- By data type : a tailored process for each format (image, sensor, text, map).
- Multi-stage fusion : combining information at different stages for better cross-analysis.
- One foundation, multiple uses : classification, detection, measurement… without starting from scratch.