Gated Path Selection Network for Semantic Segmentation

An Efficient Sampling-Based Attention Network for Semantic Segmentation

Self-attention is widely explored to model long-range dependencies in semantic segmentation. However, this operation computes pair-wise relationships between the query point and all other points, leading to prohibitive complexity. In this paper, we propose an efficient Sampling-based Attention Network which combines a novel sample method with an attention mechanism for semantic segmentation. Specifically, we design a Stochastic Sampling-based Attention Module (SSAM) to capture the relationships between the query point and a stochastic sampled representative subset from a global perspective, where the sampled subset is selected by a Stochastic Sampling Module. Compared to self-attention, our SSAM achieves comparable segmentation performance while significantly reducing computational redundancy. In addition, with the observation that not all pixels are interested in the contextual information, we design a Deterministic Sampling-based Attention Module (DSAM) to sample features from a local region for obtaining the detailed information. Extensive experiments demonstrate that our proposed method can compete or perform favorably against the state-of-the-art methods on the Cityscapes, ADE20K, COCO Stuff, and PASCAL Context datasets.

Night-Time Scene Parsing With a Large Real Dataset

Although huge progress has been made on scene analysis in recent years, most existing works assume the input images to be in day-time with good lighting conditions. In this work, we aim to address the night-time scene parsing (NTSP) problem, which has two main challenges: 1) labeled night-time data are scarce, and 2) over- and under-exposures may co-occur in the input night-time images and are not explicitly modeled in existing pipelines. To tackle the scarcity of night-time data, we collect a novel labeled dataset, named NightCity, of 4,297 real night-time images with ground truth pixel-level semantic annotations. To our knowledge, NightCity is the largest dataset for NTSP. In addition, we also propose an exposure-aware framework to address the NTSP problem through augmenting the segmentation process with explicitly learned exposure features. Extensive experiments show that training on NightCity can significantly improve NTSP performances and that our exposure-aware model outperforms the state-of-the-art methods, yielding top performances on our dataset as well as existing datasets.