Zirui Song, Guangxian Ouyang, Meng Fang, Hongbin Na, Zijing Shi, Zhenhao Chen, Fu Yujie, Zeyu Zhang, Shiyu Jiang, Miao Fang, Ling Chen, Xiuying Chen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

With the development of the industrial Internet of Things (IIoT), the amount of data generated by industrial manufacturing equipment will increase. To reduce the cost of data management while achieving secure data sharing, data owners generally upload the resulting ciphertexts to a cloud server after encrypting their data. Attribute-based encryption (ABE) is a valuable technology that implements fine-grained access control over shared information; however, its computational complexity is not suitable for resource-constrained IIoT devices, making it difficult to apply directly to an IIoT environment. To address this problem, we design a fine-grained data sharing scheme based on proxy re-encryption in IIoT. In the proposed scheme, data files are encrypted through an identity-based encryption and a data owner can authorize a semi-trusted proxy server to transform the ciphertext into an ABE ciphertext. This realizes fine-grained access control and decreases a data owner's computational cost in data sharing. In addition, the computational burden is outsourced to a cloud server, and users only need to perform simple computing operations. A formal security proof indicates the proposed scheme's selective chosen-plaintext attack security. Theoretical and experimental analyses illustrate that our construction is more efficient than previous schemes.

Local feature extraction consists of keypoint detection and local descriptor extraction. Firstly, in keypoint detector learning, existing covariance constraint loss functions cannot constrain the probability distribution shapes in local probability maps that surround keypoints. And existing auxiliary peak loss functions, which are used to alleviate the problem, impair the performance of local feature methods. To solve this problem, we propose a novel Covariant Peak constraint Loss (CP Loss) which is defined as the expectations of local probability maps' position errors. Minimizing our CP Loss can make local probability maps accurately peak at reliable keypoints. Secondly, in descriptor learning, the Neural Reprojection Error (NRE) aims at constraining dense descriptor maps of images. But we argue that only those descriptors of keypoints need to be constrained. Thus, we propose a novel Conditional Neural Reprojection Error (CNRE) that is only conditioned on keypoints. Compared with the NRE, our CNRE can achieve much higher efficiency and produce more keypoint-specific descriptors with better matching performance. We use our CP Loss and CNRE to train a local feature network named as CPCN-Feat. Experimental results show that our CPCN-Feat achieves state-of-the-art performance on four challenging benchmarks.

Most image matching methods perform poorly when encountering large scale changes in images. To solve this problem, we propose a Scale-Difference-Aware Image Matching method (SDAIM) that reduces image scale differences before local feature extraction, via resizing both images of an image pair according to an estimated scale ratio. In order to accurately estimate the scale ratio for the proposed SDAIM, we propose a Covisibility-Attention-Reinforced Matching module (CVARM) and then design a novel neural network, termed as Scale-Net, based on CVARM. The proposed CVARM can lay more stress on covisible areas within the image pair and suppress the distraction from those areas visible in only one image. Quantitative and qualitative experiments confirm that the proposed Scale-Net has higher scale ratio estimation accuracy and much better generalization ability compared with all the existing scale ratio estimation methods. Further experiments on image matching and relative pose estimation tasks demonstrate that our SDAIM and Scale-Net are able to greatly boost the performance of representative local features and state-of-the-art local feature matching methods.

The dominant approach for learning keypoint detectors relies on the covariance constraint. However, existing learned detectors sometimes extract unstable keypoints from edges. To solve this problem, we propose a novel method that exploits local saliency knowledge to train a keypoint detector, and obtain a keypoint detector, called as SRK-Net, which can extract stable and repeatable keypoints. Firstly, given an image, we propose a General Local Saliency Measure method (GLSM) to assess the local saliency value for each pixel and generate a local saliency map for this image. Then we propose a Local Salient Structure Maintaining loss (LSSM) and a two-stage progressive training manner tailored for leveraging the supervision of the covariance constraint and the local saliency maps provided by our GLSM. Experimental results show that the proposed SRK-Net performs better than all the existing keypoint detectors on HPatches dataset.

In recent years, the feature-based point cloud registration methods have attracted more attention. However, most existing methods focus on extracting features with strong antiinterference ability from a single point cloud while neglecting the differences within point cloud pairs. In this paper, unlike these methods treating each point cloud independently, we instead consider the information between point cloud pairs when extracting features. Specifically, we propose a cross-attention-based network for modeling the correlation between a pair of point clouds, where a 3D cross-attention mechanism is proposed and combined with 3D convolution elegantly for feature extraction. The extracted features achieve better robustness under various conditions, such as rotation and translation changes. Then accurate point cloud registration is achieved by matching these features. Experimental results on 3DMatch dataset show that the proposed method achieves state-of-the-art performance on feature matching and point cloud registration tasks compared with the previous feature-based methods.

Ellipses are important elements in projective geometry. Accurate extraction of ellipse information is the first step in many computer vision applications, such as ellipse-based camera calibration and camera pose estimation. At present, most ellipse detection algorithms rely on the edge features extracted by Canny, which leads to a number of wrong detection results since non-ellipse edges features are also involved. To address this problem, this paper proposes a novel ellipse marker detection neural network, called EllipseNet. Notably, we propose a new loss function to enhance the rotation perception ability of EllipseNet. Furthermore, a novel ellipse marker data enhancement method is proposed for saving the time cost of labelling ellipse parameters. Experiments show that EllipseNet can improve the detection precision of ellipse regions by more than 3% improvement compared with other SOTA general object detectors.

Loop closure detection (LCD) is an essential module for simultaneous localization and mapping (SLAM), which can correct accumulated errors after long-term explorations. The widely used bag-of-words (BoW) model can not satisfy well the requirements of both low time consumption and high accuracy for a mobile platform. In this paper, we propose a novel LCD algorithm based on motion knowledge. We give a flexible and efficient detection strategy and also give flexible and efficient combinations of a global binary feature extracted by convolutional neural network (CNN) and a hand-crafted local binary feature. We take a continuous motion model, grid-based motion statistics (GMS) and motion states as motion knowledge. Furthermore, we fuse the proposed LCD with a visual-inertial odometry (VIO) system to correct localization errors by a pose graph optimization. Comparative experiments with state-of-the-art LCD algorithms on typical datasets have been carried out, and the results demonstrate that our proposed method achieves quite high recall rates and quite high speed at 100% precision. Moreover, experimental results from VIO further validate the effectiveness of the proposed method.