# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 1 Introduction  
In recent years, convolutional networks (ConvNets) contributed a lot to the dramatic improvements in computer vision-related tasks. ConvNets not only boosted image classification related tasks [1; 2; 3; 4; 5; 6] but also made significant progress on object detection [7; 8; 9; 10; 11; 12; 13] and semantic segmentation tasks [14; 15; 16; 17; 18; 19; 20; 21; 22]. Object detection and semantic segmentation have a wide spread of applications from scene understanding to video monitoring, and they are also two important parts of autonomous driving.  
[PERSON] et al. proposed ResNet [4] in 2015, which overcame the difficulties of training very deep neural networks and let researchers achieve more and more complex models. Deep neural networks had been requiring large quantities of data to train [1] and more so after ResNet [4]. Most of us, however, are not always able to get enough data. Taking image semantic segmentation as an example, especially for driving environmental image segmentation task, it seems that people are lacking such data all the time. The famous KITTI semantics benchmark [23] just has 200 train images, and the KITTI road segmentation benchmark [24] only contains 289 training images, so it seems too few to train a very deep network, let alone researchers usually have to split training sets to make evaluations. With the high cost of labeling such data, it is hard work to create custom datasets for your own driving environment. Although there are several data augmentation methods, such as cropping, rotating, flipping, wrapping, and over-sampling [25; 26] that can help to acquire additional training data; researchers have also explored many exciting models to get more accurate segmentation results, these models still cannot work very well when the training data is really limited.  
[PERSON] and [PERSON] [27] demonstrated that the features learned by ConvNets are hierarchical, while the bottom layers focus on low-level features like corners, edges, etc., the top layers pay more attention to high-level features. Inspired by this idea, this paper proposes a model called OFA-Net (One For All, which means One model For All results required) to do driving environment images road segmentation and object detection tasks.  
In view of the fact that object detection data is much easier to label, OFA-Net utilizes KITTI 2D Object Detection Dataset [28] as a supplementary source to learn the data distribution, which can surely benefit the performance of road segmentation task. OFA-Net is a special multi-task fusion model [29] because it can output object detection and semantic segmentation results simultaneously. The model consists of three parts serving as feature extractor, detection, and segmentation, respectively. It feeds object detection and semantic segmentation data alternately, and uses two different loss functions to train each task, respectively. During this process, the model attempts to locate a proper point in the high dimensional parameter space where it performs well on both. This paper shows that by mixing object detection data with segmentation data using our \"1-N Alternation\" strategy, this unified multi-task learning [29] model can be trained faster, more accurate, with better generalization ability for the road segmentation task and high prediction confidence for the object detection task. In the meantime, training a model via fusing these two related datasets can be viewed as a data augmentation and model regularization method as well.  
To conclude briefly, the work of this paper demonstrated that object detection and semantic segmentation can benefit from each other by borrowing some fusion features when training a unified fusion model by feeding data alternately. The advantages for the OFA-net with the \"1-N Alternation\" strategy are speeding up the convergence, improving segmentation accuracy and enhancing prediction confidence for object detection.