Extensive traffic flow data are received from the loop detector networks every second, which requires us to develop an effective and efficient algorithm to predict future traffic flow. However, dynamic traffic conditions on a road are not just influenced by sequential patterns in the temporal dimension, but also by other roadways in the spatial dimension. Although many successful models have been developed in previous studies to forecast future traffic flows, most of them have shortcomings in modeling spatial and temporal dependencies. In this article, we focus on spatial-temporal factors and propose a new adaptive spatial-temporal transformer graph network (ASTTGN) to improve the accuracy of traffic forecasting by jointly modeling the spatial-temporal information of road networks. Specifically, we propose an adaptive spatial-temporal transformer module, which contains two developed adaptive transformer modules for capturing dynamic spatial dependence and temporal dependence across multiple time steps, respectively. Finally, feature fusion is performed through a gated feature aggregation layer to simulate the effect of complex spatial-temporal factors on traffic conditions. In particular, the multihead attention mechanism employed by the transformer can effectively explore the potential spatial-temporal dependence patterns in different subspaces. Experimental results on two real-world traffic data sets demonstrate the superiority of the proposed model compared to existing techniques.

Recently, many computer-aided diagnosis (CAD) methods have been proposed to help physicians automatically classify endoscopic images. However, most existing methods often result in poor performance, especially for the minority classes, when the dataset is imbalanced. In this paper, we propose a new CAD method for automated endoscopic image classification by introducing a novel class imbalance (CI) loss to the classical deep neural network (DNN). Specifically, we use the DNN to extract rich feature representations. Given that the majority class usually dominates the prediction error and influences the gradient of the network, the proposed CI loss considers both the class frequency and prediction probability of the ground-truth class to assign the weight of each sample and helps the minority classes contribute more to descending the gradient in the training process than the majority classes. Thanks to the CI loss, the network pays more attention to minority classes and hard samples. To verify the effectiveness of our proposed method, we conduct comprehensive experiments in the binary-class classification task on our collected polyp recognition dataset (22,935 images) and in the multi-class classification task on the public Hyper-Kvasir dataset (10,662 images). Experimental results show that our method is competent for the imbalanced endoscopic image classification task with good performance.

With the increasing maturity of the text-to-image and image-to-image generative models, AI-generated images (AGIs) have shown great application potential in advertisement, entertainment, education, social media, etc. Although remarkable advancements have been achieved in generative models, very few efforts have been paid to design relevant quality assessment models. In this paper, we propose a novel blind image quality assessment (IQA) network, named AMFF-Net, for AGIs. AMFF-Net evaluates AGI quality from three dimensions, i.e.“, visual quality”“, authenticity”, and “consistency”. Specifically, inspired by the characteristics of the human visual system and motivated by the observation that “visual quality” and “authenticity” are characterized by both local and global aspects, AMFF-Net scales the image up and down and takes the scaled images and original-sized image as the inputs to obtain multi-scale features. After that, an Adaptive Feature Fusion (AFF) block is used to adaptively fuse the multi-scale features with learnable weights. In addition, considering the correlation between the image and prompt, AMFF-Net compares the semantic features from text encoder and image encoder to evaluate the text-to-image alignment. We carry out extensive experiments on three AGI quality assessment databases, and the experimental results show that our AMFF-Net obtains better performance than nine state-of-the-art blind IQA methods. The results of ablation experiments further demonstrate the effectiveness of the proposed multi-scale input strategy and AFF block.

Camouflaged object detection (COD) aims to segment targeted objects that have similar colors, textures, or shapes to their background environment. Due to the limited ability in distinguishing highly similar patterns, existing COD methods usually produce inaccurate predictions, especially around the boundary areas, when coping with complex scenes. This paper proposes a Progressive Region-to-Boundary Exploration Network (PRBE-Net) to accurately detect camouflaged objects. PRBE-Net follows an encoder-decoder framework and includes three key modules. Specifically, firstly, both high-level and low-level features of the encoder are integrated by a region and boundary exploration module to explore their complementary information for extracting the object's coarse region and fine boundary cues simultaneously. Secondly, taking the region cues as the guidance information, a Region Enhancement (RE) module is used to adaptively localize and enhance the region information at each layer of the encoder. Subsequently, considering that camouflaged objects usually have blurry boundaries, a Boundary Refinement (BR) decoder is used after the RE module to better detect the boundary areas with the assistance of boundary cues. Through top-down deep supervision, PRBE-Net can progressively refine the prediction. Extensive experiments on four datasets indicate that our PRBE-Net achieves superior results over 21 state-of-the-art COD methods. Additionally, it also shows good results on polyp segmentation, a COD-related task in the medical field.

Low-light image enhancement (LIE) is important for many high-level vision tasks as the poor visibility of underexposed images can severely degrade the performance of the subsequent image recognition, analysis, etc. Although recent deep-learning-based LIE methods exhibit promising performance, most of them require a large number of paired training images, thereby limiting the practicability to real scenarios. In this paper, we propose a pseudo-supervised LIE method with the integration of mutual learning. Specifically, for the given low-light image, we first use a quadratic curve to generate a pseudo-clear image, which is served as the auxiliary ground truth for supervision, then the pseudo-paired images are simultaneously input to two parallel homogeneous branches to learn the expected enhanced result through the knowledge distillation of two branches via mutual learning. As both the generated image and the input low-light image underlies the desired solution, the mutual learning strategy enables the two branches learn from each other and produce the final results. Extensive experiments demonstrate that the proposed method outperforms most existing unsupervised LIE methods in terms of both qualitative and quantitative evaluations, and also achieves competitive performance against many supervised and semi-supervised methods.