278 papers with code • 4 benchmarks • 39 datasets
Scene Understanding is something that to understand a scene. For instance, iPhone has function that help eye disabled person to take a photo by discribing what the camera sees. This is an example of Scene Understanding.
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Semantic segmentation is an important tool for visual scene understanding and a meaningful measure of uncertainty is essential for decision making.
In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image.
Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications.
Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored.
We leverage this scaling to train an agent for 2. 5 Billion steps of experience (the equivalent of 80 years of human experience) -- over 6 months of GPU-time training in under 3 days of wall-clock time with 64 GPUs.