Camouflaged Object Detection (COD) aims to segment objects that blend in with their surroundings. Most existing methods mainly tackle this issue by a single-stage framework, which tends to degrade performance in the face of small objects, low-contrast objects and objects with diverse appearances. In this paper, we propose a novel Progressive Enhancement Network (PENet) for COD by imitating the human visual detection system, which follows a three-stage detection process: locate objects, refine textures and restore boundary. Specifically, our PENet contains three key modules, i.e., the object location module (OLM), the group attention module (GAM) and the context feature restoration module (CFRM). The OLM is designed to position the object globally, the GAM is developed to refine both high-level semantic and low-level texture feature representation, and the CFRM is leveraged to effectively aggregate multi-level features for progressively restoring the clear boundary. Extensive results demonstrate that our PENet significantly outperforms 32 state-of-the-art methods on four widely used benchmark datasets

Human action recognition in videos is an important task with a broad range of applications. In this study, we improve the performance of recurrent attention convolutional neural network (RACNN) by proposing a novel model, “attention-again”. We consider the nature of video frames as sequences, which will cause the change of regions of interest in the frame, thus we cannot use an attention mechanism similar to that in images. “Attention-again” model is a variant from traditional attention model for recognizing human activities and is embedded in two long short-term memory (LSTM) layers. Different from hierarchal LSTM which change the LSTM structure to combine the hidden states from two LSTM layers, our proposals introduce “attention-again” model to avoid the change of LSTM structure. Furthermore, this model not only learns the relations in each frame, but also obtains the relations among all frames, and these relations instruct the next learning stage. Therefore, our proposed model outperform the baseline and is superior to methods with the same experimental conditions on three benchmark datasets: UCF-11, HMDB-51 and UCF-101. To understand how the model works, we also visualize the region of interest in the frame.

With the rapid development of remote sensing technology, the application of convolutional neural networks in remote sensing object detection has become very widespread, and some multimodal feature fusion networks have also been proposed in recent years. However, these methods generally do not consider the long-tailed problem that is widely present in remote sensing images, which limits the further improvement of model detection performance. To solve this problem, we propose a novel long-tailed object detection method for multimodal remote sensing images, which can effectively fuse the complementary information of visible light and infrared images and adapt to the imbalance between positive and negative samples of different categories. Firstly, the dynamic feature fusion module (DFF) based on image entropy can dynamically adjust the fusion coefficient according to the information content of different source images, retaining more key feature information for subsequent object detection. Secondly, the instance-balanced mosaic (IBM) data augmentation method balances instance sampling during data augmentation, providing more sample features for the model and alleviating the negative impact of data distribution imbalance. Finally, class-balanced BCE loss (CBB) can not only consider the learning difficulty of specific instances but also balances the learning difficulty between categories, thereby improving the model’s detection accuracy for tail instances. Experimental results on three public benchmark datasets show that our proposed method achieves state-of-the-art performance; in particular, the optimization of the long-tailed problem enables the model to meet various application scenarios of remote sensing image detection.