Learning to Sense (L2S)

The workshop explores the joint optimization of sensors and machine learning models, pushing beyond traditional paradigms of data acquisition and processing. We aim to rethink the foundations of how machines sense the world by replacing hand-crafted ISPs, leveraging learnable sensor layouts, and adopting task-driven sensing strategies.

We welcome original contributions and position papers on the following topics (non-exhaustive):

• Sensor optimization for e.g. computer vision (bit-depth, pixel layouts, color filter design)

• RAW-to-task or RAW-to-label approaches for visual tasks

• Co-design of neural networks and sensor hardware

• Low-bit and energy-efficient sensing for embedded or mobile devices

• Benchmarks, datasets, and metrics for evaluating sensor-model pipelines

• Generalization and robustness of sensor-model systems in real-world conditions

• Failure case studies and negative results in joint optimization pipelines

Join us to engage with cutting-edge research and cross-disciplinary discussions that are shaping the future of sensor systems for real-world deployment across mobile, embedded, and autonomous platforms.