Weather Recognition plays an important role in our daily lives and many computer vision applications. However, recognizing the weather conditions from a single image remains challenging and has not been studied thoroughly. Generally, most previous works treat weather recognition as a single-label classification task, namely, determining whether an image belongs to a specific weather class or not. This treatment is not always appropriate, since more than one weather conditions may appear simultaneously in a single image. To address this problem, we make the first attempt to view weather recognition as a multi-label classification task, i.e., assigning an image more than one labels according to the displayed weather conditions. Specifically, a CNN-RNN based multi-label classification approach is proposed in this paper. The convolutional neural network (CNN) is extended with a channel-wise attention model to extract the most correlated visual features. The Recurrent Neural Network (RNN) further processes the features and excavates the dependencies among weather classes. Finally, the weather labels are predicted step by step. Besides, we construct two datasets for the weather recognition task and explore the relationships among different weather conditions. Experimental results demonstrate the superiority and effectiveness of the proposed approach. The new constructed datasets will be available at this https URL.
In recent years, convolutional neural networks have shown overwhelming performance in a variety of computer vision tasks, such as image classification @cite_5 , object detection @cite_16 , semantic segmentation @cite_27 , etc. Several excellent architectures of CNNs are proposed including AlexNet @cite_5 , VGGNet @cite_44 and ResNet @cite_41 , which outperform the traditional approaches to a large extent. Inspired by the great success of CNNs, a few of works attempt to apply CNNs to weather recognition task. Elhoseiny @cite_15 directly fine-tuned AlexNet @cite_5 on a two-class weather classification dataset released by @cite_22 , and achieved a better result. Lu @cite_54 combined hand-crafted weather features with CNNs extracted features, and further improved the classification performance. While as discussed in @cite_54 , there is no closed boundaries among weather classes. Multiple weather conditions may appear simultaneously. Therefore, all the above approaches suffer from the information loss when they treat weather recognition as a single label classification problem. Li @cite_12 proposed to use auxiliary semantic segmentation of weather cues to comprehensively describe the weather conditions. This strategy can alleviate the problem of information loss, while the segmentation mask is not intuitive for humans.
@cite_22 @cite_41 @cite_54 @cite_44 @cite_27 @cite_5 @cite_15 @cite_16 @cite_12
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Weather recognition is important in practice, while this task has not been thoroughly explored so far. The current trend of dealing with this task is treating it as a single classification problem, i.e., determining whether a given image belongs to a certain weather category or not. However, weather recognition differs significantly from traditional image classification, since several weather features may appear simultaneously. In this case, a simple classification result is insufficient to describe the weather condition. To address this issue, we propose to provide auxiliary weather related information for comprehensive weather description. Specifically, semantic segmentation of weather-cues, such as blue sky and white clouds, is exploited as an auxiliary task in this paper. Moreover, a convolutional neural network (CNN) based multi-task framework is developed which aims to concurrently tackle weather category classification task and weather-cues segmentation task. Due to the intrinsic relationships between these two tasks, exploring auxiliary semantic segmentation of weather-cues can also help to learn discriminative features for the classification task, and thus obtain superior accuracy. To verify the effectiveness of the proposed approach, extra segmentation masks of weather-cues are generated manually on an existing weather image dataset. Experimental results have demonstrated the superior performance of our approach. The enhanced dataset, source codes and pre-trained models are available at https: github.com wzgwzg Multitask_Weather. Convolutional neural networks (CNNs) have shown great performance as general feature representations for object recognition applications. However, for multi-label images that contain multiple objects from different categories, scales and locations, global CNN features are not optimal. In this paper, we incorporate local information to enhance the feature discriminative power. In particular, we first extract object proposals from each image. With each image treated as a bag and object proposals extracted from it treated as instances, we transform the multi-label recognition problem into a multi-class multi-instance learning problem. Then, in addition to extracting the typical CNN feature representation from each proposal, we propose to make use of ground-truth bounding box annotations (strong labels) to add another level of local information by using nearest-neighbor relationships of local regions to form a multi-view pipeline. The proposed multi-view multiinstance framework utilizes both weak and strong labels effectively, and more importantly it has the generalization ability to even boost the performance of unseen categories by partial strong labels from other categories. Our framework is extensively compared with state-of-the-art handcrafted feature based methods and CNN based methods on two multi-label benchmark datasets. The experimental results validate the discriminative power and the generalization ability of the proposed framework. With strong labels, our framework is able to achieve state-of-the-art results in both datasets. Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial pyramid matching) contain much more local discriminative and structural information, which is implicitly embedded in the images. To further improve the performance, in this paper, we propose to combine CNN with dictionary-based models for scene recognition and visual domain adaptation (DA). Specifically, based on the well-tuned CNN models (e.g., AlexNet and VGG Net), two dictionary-based representations are further constructed, namely, mid-level local representation (MLR) and convolutional Fisher vector (CFV) representation. In MLR, an efficient two-stage clustering method, i.e., weighted spatial and feature space spectral clustering on the parts of a single image followed by clustering all representative parts of all images, is used to generate a class-mixture or a class-specific part dictionary. After that, the part dictionary is used to operate with the multiscale image inputs for generating mid-level representation. In CFV, a multiscale and scale-proportional Gaussian mixture model training strategy is utilized to generate Fisher vectors based on the last convolutional layer of CNN. By integrating the complementary information of MLR, CFV, and the CNN features of the fully connected layer, the state-of-the-art performance can be achieved on scene recognition and DA problems. An interested finding is that our proposed hybrid representation (from VGG net trained on ImageNet) is also complementary to GoogLeNet and or VGG-11 (trained on Place205) greatly. Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a significant decrease in the initial image resolution. Here, we present RefineNet, a generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. In this way, the deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. The individual components of RefineNet employ residual connections following the identity mapping mindset, which allows for effective end-to-end training. Further, we introduce chained residual pooling, which captures rich background context in an efficient manner. We carry out comprehensive experiments and set new state-of-the-art results on seven public datasets. In particular, we achieve an intersection-over-union score of 83.4 on the challenging PASCAL VOC 2012 dataset, which is the best reported result to date.