# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 2 Related Work
### Classification, Detection, and Segmentation  
**Classification** Recent years most image classification models utilize ConvNets after the success of AlexNet [1]. Some researchers claimed that network depth is crucial for network performance [3,5]. Unfortunately, training a very deep neural network was difficult due to the problem of vanishing/exploding gradients. ResNet [4] proposed by [PERSON] et al. is the state-of-the-art structure, which allows people to train very deep networks without worrying about the problems mentioned above. Some researchers pointed out that Deep ConvNets learn image features hierarchically, which is, convolutional kernels of bottom layers extract low-level features while the top layers are to combine the low-level features as high-level representations [27].  
**Detection** Traditional methods for object detection usually followed a two-step strategy. Firstly, the model proposed numerous region proposals [30], then classified them to get correct predictions. RCNN [7] and Fast RCNN [8] were the typical representations of this strategy. These methods were easy to understand and implement. Nevertheless, due to the multi-stage training pipeline, it is too slow and contains too much repetitive computation and cannot be trained end-to-end. Although the Faster RCNN [9] model made use of RPN (Region Proposal Network) and solved these problems partially, it was still too slow to use. Fortunately, there are several models that have been proposed to use only one Deep ConvNet trainable end-to-end to make detections directly, such as YOLO models [10,11,13] and SSD [12]. These models are superior to those region proposal-based models mainly for their fast train and inference speed, and thus more appropriate for real-time object detection work. Thus, the OFA-Net model leverages a lot from the YOLO models [10,11,13].  
**Segmentation** For the semantic segmentation task, some early approaches attempted to adapt the model architectures designed for image classification directly to pixel-wise label prediction [31]. Despite the results surpassing traditional models, which heavily relied on low-level features hand-engineered by humans [32; 33; 34; 35], they still appeared fairly coarse [17]. Researchers tried a lot to fine the segmentation results. FCN (Fully Convolutional Network) [14] replaced the last fully connected layer with a convolutional layer and then added an up-sampling layer to recover the image size. The biggest problem of FCN is the network lost a lot of information due to the pooling layers, thus led the results not fine enough. To aggregate multi-scale contextual information with no resolution lost, [PERSON] and [PERSON] created dilated convolution modules [16]. To fine the prediction result, [PERSON] et al. designed an encoder-decoder architecture [15], in which the encoder network was identical to normal ConvNets and the decoder network was to learn the map between the low-resolution encoder and high-resolution feature maps whose size is identical to the input images. Recently, [PERSON] et al. [21; 22] combined the dilated convolutional module and the encoder-decoder architecture together and borrowed the idea of the SPP (Spatial Pyramid Pooling) [6; 20] to create an ASPP module (Atrous Spatial Pyramid Pooling) for better performance on multiple scale segmentation.