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Title: FasterSeg: Searching for Faster Real-time Semantic Segmentation

Paper content:
We present FasterSeg, an automatically designed semantic segmentation network with not only state-of-the-art performance but also faster speed than current methods.
Utilizing neural architecture search (NAS), FasterSeg is discovered from a novel and broader search space integrating multi-resolution branches, that has been recently found to be vital in manually designed segmentation models.
To better calibrate the balance between the goals of high accuracy and low latency, we propose a decoupled and fine-grained latency regularization, that effectively overcomes our observed phenomenons that the searched networks are prone to "collapsing" to low-latency yet poor-accuracy models.
Moreover, we seamlessly extend FasterSeg to a new collaborative search (co-searching) framework, simultaneously searching for a teacher and a student network in the same single run.
The teacher-student distillation further boosts the student model’s accuracy.
Experiments on popular segmentation benchmarks demonstrate the competency of FasterSeg.
For example, FasterSeg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy.
Semantic segmentation predicts pixel-level annotations of different semantic categories for an image.
Despite its performance breakthrough thanks to the prosperity of convolutional neural networks (CNNs) (Long et al., 2015) , as a dense structured prediction task, segmentation models commonly suffer from heavy memory costs and latency, often due to stacking convolutions and aggregating multiple-scale features, as well as the increasing input image resolutions.
However, recent years witness the fast-growing demand for real-time usage of semantic segmentation, e.g., autonomous driving.
Such has motivated the enthusiasm on designing low-latency, more efficient segmentation networks, without sacrificing accuracy notably (Zhao et al., 2018; Yu et al., 2018a) .
The recent success of neural architecture search (NAS) algorithms has shed light on the new horizon in designing better semantic segmentation models, especially under latency of other resource constraints.
Auto-DeepLab (Liu et al., 2019a) first introduced network-level search space to optimize resolutions (in addition to cell structure) for segmentation tasks.
and Li et al. (2019) adopted pre-defined network-level patterns of spatial resolution, and searched for operators and decoders with latency constraint.
Despite a handful of preliminary successes, we observe that the successful human domain expertise in designing segmentation models appears to be not fully integrated into NAS frameworks yet.
For example, human-designed architectures for real-time segmentation (Zhao et al., 2018; Yu et al., 2018a) commonly exploit multi-resolution branches with proper depth, width, operators, and downsample rates, and find them contributing vitally to the success: such flexibility has not been unleashed by existing NAS segmentation efforts.
Furthermore, the trade-off between two (somewhat conflicting) goals, i.e., high accuracy and low latency, also makes the search process unstable and prone to "bad local minima" architecture options.
As the well-said quote goes: "those who do not learn history are doomed to repeat it".
Inheriting and inspired by the successful practice in hand-crafted efficient segmentation, we propose a novel NAS framework dubbed FasterSeg, aiming to achieve extremely fast inference speed and competitive accuracy.
We designed a special search space capable of supporting optimization over multiple branches of different resolutions, instead of a single backbone.
These searched branches are adaptively aggregated for the final prediction.
To further balance between accuracy versus latency and avoiding collapsing towards either metric (e.g., good latency yet poor accuracy), we design a decoupled and fine-grained latency regularization, that facilitates a more flexible and effective calibration between latency and accuracy.
Moreover, our NAS framework can be easily extended to a collaborative search (co-searching), i.e., jointly searching for a complex teacher network and a light-weight student network in a single run, whereas the two models are coupled by feature distillation in order to boost the student's accuracy.
We summarize our main contributions as follows:
• A novel NAS search space tailored for real-time segmentation, where multi-resolution branches can be flexibility searched and aggregated.
• A novel decoupled and fine-grained latency regularization, that successfully alleviates the "architecture collapse" problem in the latency-constrained search.
• A novel extension to teacher-student co-searching for the first time, where we distill the teacher to the student for further accuracy boost of the latter.
• Extensive experiments demonstrating that FasterSeg achieves extremely fast speed (over 30% faster than the closest manually designed competitor on CityScapes) and maintains competitive accuracy.
We introduced a novel multi-resolution NAS framework, leveraging successful design patterns in handcrafted networks for real-time segmentation.
Our NAS framework can automatically discover FasterSeg, which achieved both extremely fast inference speed and competitive accuracy.
Our search space is intrinsically of low-latency and is much larger and challenging due to flexible searchable expansion ratios.
More importantly, we successfully addressed the "architecture collapse" problem, by proposing the novel regularized latency optimization of fine-granularity.
We also demonstrate that by seamlessly extending to teacher-student co-searching, our NAS framework can boost the student's accuracy via effective distillation.
A STEM AND HEAD MODULE Stem: Our stem module aims to quickly downsample the input image to 1 8 resolution while increasing the number of channels.
The stem module consists of five 3 × 3 convolution layers, where the first, second, and fourth layer are of stride two and double the number of channels.
Head: As shown in Figure 1 , feature map of shape (C 2s × H × W ) is first reduced in channels by a 1 × 1 convolution layer and bilinearly upsampled to match the shape of the other feature map (C s × 2H × 2W ).
Then, two feature maps are concatenated and fused together with a 3 × 3 convolution layer.
Note that we not necessarily have C 2s = 2C s because of the searchable expansion ratios.

assistant: text: We present a real-time segmentation model automatically discovered by a multi-scale NAS framework, achieving 30% faster than state-of-the-art models.