Quartet Logic: A Four-Step Reasoning framework for advancing Short Text Classification

Short Text Classification (STC) is crucial for processing and comprehending the brief but substantial content prevalent on contemporary digital platforms. The STC encounters difficulties in grasping semantic and syntactic intricacies, an issue that is apparent in traditional pre-trained language models. Although Graph Convolutional Networks enhance performance by integrating external knowledge bases, these methods are limited by the quality and extent of the knowledge applied. Recently, the emergence of Large Language Models (LLMs) and Chain-of-Thought (CoT) has significantly improved the performance of complex reasoning tasks. However, some studies have highlighted the limitations of their application in fundamental NLP tasks. Consequently, this study sought to employ CoT to investigate the capabilities of LLMs in STC tasks. This study introduces Quartet Logic: A Four-Step Reasoning (QLFR) framework. This framework primarily incorporates Syntactic and Semantic Enrichment CoT, effectively decomposing the STC task into four distinct steps: (i) essential concept identification, (ii) common-sense knowledge retrieval, (iii) text rewriting, and (iv) classification. This elicits the inherent knowledge and abilities of LLMs to address the challenges in STC. Surprisingly, we found that QLFR can also improve the performance of smaller models. Therefore, we developed a CoT-Driven Multi-task learning (QLFR-CML) method to facilitate the knowledge transfer from LLMs to smaller models. Extensive experimentation across six short-text benchmarks validated the efficacy of the proposed methods. Notably, QLFR achieved state-of-the-art performance on all datasets, with significant improvements, particularly on the Ohsumed and TagMyNews datasets.

HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images

Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, proving critical for endeavours such as 3D urban modelling, MR and autonomous driving. Traditional methods utilise stereo matching or multisensor fusion, both well-established techniques that typically necessitate multiple images from varying perspectives and adjunct sensors like SAR, leading to substantial deployment costs. Single image height estimation has emerged as an attractive alternative, boasting a larger data source variety and simpler deployment. However, current methods suffer from limitations such as fixed receptive fields, a lack of global information interaction, leading to noticeable instance-level height deviations. The inherent complexity of height prediction can result in a blurry estimation of object edge depth when using mainstream regression methods based on fixed height division. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification-regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification-regression Height Generator (ICG). MIB supplements the fixed sample grid in CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification-regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness.