Dialogue intent classification is a pivotal task in natural language understanding, crucial for effective human-computer interactions. Despite significant progress in structural modeling of dialogues and texts, existing research still has several limitations: intention labels are treated as independent entities, ignoring the hierarchical relationships and dependencies among them and making it hard to make accurate predictions on fine-grained labels. In this paper, we propose a Hierarchical Label-aware Dialogue Intent Classification model (HLDIC) for dialogue intent classification. Specifically, we leverage the hierarchical relationships of labels by introducing a coarse-grained label classification auxiliary task. A hierarchical adaptive attention mechanism is proposed, which employs gate mechanisms to guide the model in recognizing keywords and vital adjacent pairs. To further explore label dependencies, a hierarchy-aware mechanism is proposed to use a mask matrix to allow the model to focus on the correct fine-grained labels within the corresponding coarse-grained labels and partially suppress the noise from other coarse-grained labels. Experimental results on public CCL2018-Task1 corpus show the superior performance of HLDIC.

The diversity of building architecture styles of global cities situated on various landforms, the degraded optical imagery affected by clouds and shadows, and the significant inter-class imbalance of roof types pose challenges for designing a robust and accurate building roof instance segmentor. To address these issues, we propose an effective framework to fulfill semantic interpretation of individual buildings with high-resolution optical satellite imagery. Specifically, the leveraged domain adapted pretraining strategy and composite dual-backbone greatly facilitates the discriminative feature learning. Moreover, new data augmentation pipeline, stochastic weight averaging (SWA) training and instance segmentation based model ensemble in testing are utilized to acquire additional performance boost. Experiment results show that our approach ranks in the first place of the 2023 IEEE GRSS Data Fusion Contest (DFC) Track 1 test phase (mAP50:50.6%). Note-worthily, we have also explored the potential of multimodal data fusion with both optical satellite imagery and SAR data.

Unifying the correlative single-view satellite image building extraction and height estimation tasks indicates a promising way to share representations and acquire generalist model for large-scale urban 3D reconstruction. However, the common spatial misalignment between building footprints and stereo-reconstructed nDSM height labels incurs degraded performance on both tasks. To address this issue, we propose a Height-hierarchy Guided Dual-decoder Network (HGDNet) to estimate building height. Under the guidance of synthesized discrete height-hierarchy nDSM, auxiliary height-hierarchical building extraction branch enhance the height estimation branch with implicit constraints, yielding an accuracy improvement of more than 6% on the DFC 2023 track2 dataset. Additional two-stage cascade architecture is adopted to achieve more accurate building extraction. Experiments on the DFC 2023 Track 2 dataset shows the superiority of the proposed method in building height estimation (δ1:0.8012), instance extraction (AP50:0.7730), and the final average score 0.7871 ranks in the first place in test phase.

Dialogue intent classification is a fundamental and essential task in dialogue systems. Although sentence-level and document-level text classification have made dramatic progress in recent years with the help of deep learning technology, dialogue-level classification remains challenging. Dialogue has unique characteristics that distinguish it from other types of text. Dialogue is interactive, with feedback between speakers, and turn-taking. These unique features suggest that model architecture should take dialogue structure into account to learn a better representation. In this paper we propose an Adjacency Pairs-Aware Hierarchical Attention Network (AP-HAN) for dialogue intent classification. A dialogue reconstruction strategy is designed to match the question and answer utterances properly and then make the dialogue to be presented as a sequence of adjacent pairs. Then, the adjacency pairs features are incorporated into the hierarchical attention network. Experimental results on public CCL2018-Task1 corpus show the better performance of the proposed model.

The authors study an optimal searcher path problem of the following type. A team of searchers tries to find an immobile target on a unit network (in a unit network, all edges are of unit length). The target is located at some point on this network, and its location probability distribution is known to the searcher team. Searchers move along the edges on the network at a constant speed. When one searcher passes the target location, detection happens and the search ends. The objective is to find paths for this team of searchers to minimize the expected search time. A linear binary programming model is built to find the optimal search strategy. Computational experiments are conducted to test the capability of this model, and to analyze how searcher team size and target distribution might affect the search time. Analytical results are given for a special problem scenario: one searcher searching for a uniformly distributed target on a tree. We prove that, any depth‐first search path is optimal for a given searcher start point, and the best start point is one of the leaf nodes.