We present an unsupervised multi-source domain adaptive semantic segmentation approach in unstructured and unconstrained traffic environments. We propose a novel training strategy that alternates between single-source domain adaptation (DA) and multi-source distillation, and also between setting up an improvised cost function and optimizing it. In each iteration, the single-source DA first learns a neural network on a selected source, which is followed by a multi-source fine-tuning step using the remaining sources. We call this training routine the Alternating-Incremental ("Alt-Inc") algorithm. Furthermore, our approach is also boundless i.e. it can explicitly classify categories that do not belong to the training dataset (as opposed to labeling such objects as "unknown"). We have conducted extensive experiments and ablation studies using the Indian Driving Dataset, CityScapes, Berkeley DeepDrive, GTA V, and the Synscapes datasets, and we show that our unsupervised approach outperforms other unsupervised and semi-supervised SOTA benchmarks by 5.17% - 42.9% with a reduced model size by up to 5.2x.
Under Display Cameras present a promising opportunity for phone manufacturers to achieve bezel-free displays by positioning the camera behind semi-transparent OLED screens. Unfortunately, such imaging systems suffer from severe image degradation due to light attenuation and diffraction effects. In this work, we present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems. A Low-Resolution Network first restores image quality at low-resolution, which is subsequently used by the Guided Filter Network as a filtering input to produce a high-resolution output. Besides the initial downsampling, our low-resolution network uses multiple, parallel atrous convolutions to preserve spatial resolution and emulates multi-scale processing. Our approach's ability to directly train on megapixel images results in significant performance improvement. We additionally propose a simple simulation scheme to pre-train our model and boost performance. Our overall framework ranks 2nd and 5th in the RLQ-TOD'20 UDC Challenge for POLED and TOLED displays, respectively.
This paper is the report of the first Under-Display Camera (UDC) image restoration challenge in conjunction with the RLQ workshop at ECCV 2020. The challenge is based on a newly-collected database of Under-Display Camera. The challenge tracks correspond to two types of display: a 4k Transparent OLED (T-OLED) and a phone Pentile OLED (P-OLED). Along with about 150 teams registered the challenge, eight and nine teams submitted the results during the testing phase for each track. The results in the paper are state-of-the-art restoration performance of Under-Display Camera Restoration. Datasets and paper are available at https://yzhouas.github.io/projects/UDC/udc.html.