Events Are Not Unit Tokens: Learning Threshold-Consistent Representations for Event Cameras

Event cameras encode a brightness change as an asynchronous event once the log-intensity variation reaches a contrast threshold C. This sensing rule gives event streams their high temporal resolution, but it also means that an event is not a threshold-independent unit token: the same visual change can produce many events at a low threshold and only a few events at a high threshold. Most event representations aggregate counts, timestamps, polarities, or recurrent tokens without preserving the physical contrast carried by each event, so a change in sensor threshold can be mixed with a change in visual content. To address this issue, we propose Pixel Contrast Flow (PCF), a threshold-consistent event representation that uses physical contrast mass as the state-update variable. PCF combines contrast-mass encoding, a semigroup contrast flow, and pixel-local temporal integration before spatial mixing. The resulting update is unchanged when the same contrast is split into finer event quanta, while pixel locality removes sensitivity to threshold-induced reordering across different pixels. On the paired Caltech101-CT benchmark, PCF maintains a nominal accuracy of 49.59% while achieving the best accuracy at 0.25C_0, 0.5C_0, and 4C_0, the highest worst-case OOD accuracy of 19.06%, and the lowest paired feature drift with a RelL2 of 0.215. Detailed robustness tests further show that PCF obtains the best Global Mean, Global Worst, polarity-asymmetry accuracy, and competitive pixel-mismatch performance. These results support a simple principle: event representations should follow the physical quantity measured by the sensor rather than the arbitrary number of emitted tokens.