Analyzing the semantic similarity of cross-lingual texts is a crucial part of natural language processing (NLP). The computation of semantic similarity is essential for a variety of tasks such as evaluating machine translation systems, quality checking human translation, information retrieval, plagiarism checks, etc. In this paper, we propose a method for measuring the semantic similarity of Kannada–English sentence pairs that uses embedding space alignment, lexical decomposition, word order, and a convolutional neural network. The proposed method achieves a maximum correlation of 83% with human annotations. Experiments on semantic matching and retrieval tasks resulted in promising results in terms of precision and recall.

Trajectory prediction is a critical component of autonomous driving systems, enabling vehicles to anticipate the motion of surrounding agents and make safe, informed navigation decisions. This paper presents BEV-IMTP, a lightweight instance mapping and trajectory prediction network designed for Bird’s-Eye-View representations. The proposed framework adopts a streamlined architecture with customized core layers and a minimal-parameter backbone, significantly reducing computational overhead while making it a promising candidate for latency-sensitive autonomous driving applications. BEV-IMTP achieves a 4.6% improvement in semantic map mIoU and a 1.8% gain in instance motion mIoU compared to state-of-the-art methods. On the nuScenes benchmark, the proposed model attains an overall semantic map mIoU of 62.1%, with strong class-wise performance of 63.4% for divider, 58.3% for pedestrian crossing, and 64.6% for boundary regions. Despite its high accuracy, BEV-IMTP remains computationally efficient, requiring only 34.5 million parameters and 88 GFLOPs, and supports inference at 3.1 FPS on a single Tesla V100 GPU. The model is trained on the nuScenes dataset and further evaluated on the Lyft dataset, demonstrating robust generalization across datasets captured using cameras with varying configurations. Extensive experiments on publicly available trajectory prediction benchmarks validated the effectiveness and efficiency of the proposed BEV-IMTP framework.

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition.