Visual grounding is the task of locating the relevant region in an image based on a textual description. Most existing methods rely on pre-trained visual and text encoder to extract features from images and text, which are fed into a fusion module to obtain fused features. To obtain high-quality fused features, researchers often design various complex multi-modal fusion modules. These fused features are processed using an encoder-decoder architecture to produce the final output. However, in the training process, the optimization objective is typically centered around the final predicted outputs, such as bounding boxes or instance segmentation masks, while the quality of the fused features often receives less direct attention. Therefore, the gradient usually needs to traverse a relatively long path when being backpropagated to the multi-modal fusion module, leading to diminished optimization effectiveness. In this paper, we propose ALVG, a simple and efficient visual grounding framework. We design a novel loss function to directly supervise the attention mechanism of multi-modal fusion modules, along with a simple but effective text-guided image enhancement module to complement it. The enhanced features are directly used for instance segmentation tasks, as well as object detection tasks. Experiments on six widely used Visual Grounding datasets, including RefCOCO/+/g, ReferIt, Flickr30K, and GRefCOCO, demonstrate the superiority of ALVG. Our method not only improves efficiency and convergence speed but also achieves state-of-the-art performance on these benchmarks.

We propose a novel model‐free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real‐world image deraining, dubbed MUL‐Derain. Beyond existing unsupervised deraining efforts, MUL‐Derain leverages a model‐free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL‐Derain can efficiently compute spatial coherence and global interactions by modeling long‐range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL‐Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real‐world datasets demonstrate that our MUL‐Derain obtains state‐of‐the‐art performance over un/semisupervised methods and exhibits competitive advantages over the fully‐supervised ones.

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low‐level adaptation methods that combine one detector with an additional dehazing network and (ii) high‐level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question—if combining both low‐level adaptation and high‐level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High‐Low Adaptation Object Detection paradigm (HLA‐HOD) in hazy weather conditions. By combining both low‐level adaptation and high‐level adaptation, HLA‐HOD achieves superior performance on hazy images without requiring ground‐truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state‐of‐the‐art low‐level and high‐level adaptation methods by a large margin both quantitatively and qualitatively.

Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity‐based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. This algorithm uses the correlation between features to select the best subset. Then, a hybrid partitioning strategy is introduced in the negative selection algorithm (NSA), which divides the feature space into a grid based on the sample distribution density and generates specific candidate detectors in the boundary grid to effectively mitigate the holes caused by boundary diversity. Finally, the NSA is improved by self‐set clustering and a novel gray wolf optimizer to achieve adaptive adjustment of the detector radius and position. The results show that the proposed NSA algorithm based on mixed hierarchical division and gray wolf optimization (MDGWO‐NSA) achieves a higher detection rate, lower false alarm rate, and better generation quality than other network anomaly detection algorithms.