Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power.Simultaneously, a critical pain point arises as several computer vision applications are deployed on low-power embedded devices, necessitating real-time processing capabilities.This challenge intensifies for semantic segmentation, a dense prediction task demanding substantial memory and computational resources.This thesis explores techniques to streamline real-time segmentation networks, enhance their efficiency, and deal with potential ambiguity.First, we introduce a latency-aware segmentation metric, a measure that combines the mean Intersection over Union with the network processing time, providing a practical metric for applied settings.Emphasis is placed on the concept of "anticipation" in real-time networks - these systems should be capable of predicting future input segmentation.Consequently, we then design an anticipatory convolutional network incorporating an inventive convolution layer. This novel layer reduces computation by reusing features from previous video frame computations, exploiting their temporal coherence.Next, we present a method to accelerate transformer-based segmentation networks called `patch-pausing'. This technique halts the processing of image patches deemed to be already correctly segmented by assessing the network's confidence in its prediction.Remarkably, our experimental results indicate that more than half of the patches can be paused early in the process, with a minimal impact on segmentation accuracy.This study concludes with the introduction of a discrete diffusion model for segmentation. This model allows for the sampling of multiple potential segmentations for a given input while accurately following the training data distribution.Combining this diffusion model within an autoregressive scheme, we successfully showcase its capacity to generate long-term future predictions of segmentation.The implementation and evaluation of these approaches contribute to the ongoing efforts to improve real-time segmentation networks and facilitate more efficient deployment of computer vision applications on low-power devices.