In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized Large Language Model tailored for urban and spatial planning. Developed through collaborative efforts with institutions like the Chinese Academy of Urban Planning, PlanGPT leverages a customized local database retrieval framework, domain-specific fine-tuning of base models, and advanced tooling capabilities. Empirical tests demonstrate that PlanGPT has achieved advanced performance, delivering responses of superior quality precisely tailored to the intricacies of urban planning.
In this work, we propose a dynamic landing solution without the need for onboard exteroceptive sensors and an expensive computation unit, where all localization and control modules are carried out on the ground in a non-inertial frame. Our system starts with a relative state estimator of the aerial robot from the perspective of the landing platform, where the state tracking of the UAV is done through a set of onboard LED markers and an on-ground camera; the state is expressed geometrically on manifold, and is returned by Iterated Extended Kalman filter (IEKF) algorithm. Subsequently, a motion planning module is developed to guide the landing process, formulating it as a minimum jerk trajectory by applying the differential flatness property. Considering visibility and dynamic constraints, the problem is solved using quadratic programming, and the final motion primitive is expressed through piecewise polynomials. Through a series of experiments, the applicability of this approach is validated by successfully landing 18 cm x 18 cm quadrotor on a 43 cm x 43 cm platform, exhibiting performance comparable to conventional methods. Finally, we provide comprehensive hardware and software details to the research community for future reference.
Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects of varying scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region, thus providing sufficient and balanced positive samples for training. Furthermore, to address the challenge of accurately regressing objects with varying scales, we put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code will be available at https://github.com/Say2L/DCDet.git.
It is challenging to model and control a tail-sitter unmanned aerial vehicle (UAV) because its blended wing body generates complicated nonlinear aerodynamic effects, such as wing lift, fuselage drag, and propeller-wing interactions. We therefore devised a hybrid aerodynamic modeling method and model predictive control (MPC) design for a quadrotor tail-sitter UAV. The hybrid model consists of the Newton-Euler equation, which describes quadrotor dynamics, and a feedforward neural network, which learns residual aerodynamic effects. This hybrid model exhibits high predictive accuracy at a low computational cost and was used to implement hybrid MPC, which optimizes the throttle, pitch angle, and roll angle for position tracking. The controller performance was validated in real-world experiments, which obtained a 57% tracking error reduction compared with conventional nonlinear MPC. External wind disturbance was also introduced and the experimental results confirmed the robustness of the controller to these conditions.
Neural construction models have shown promising performance for Vehicle Routing Problems (VRPs) by adopting either the Autoregressive (AR) or Non-Autoregressive (NAR) learning approach. While AR models produce high-quality solutions, they generally have a high inference latency due to their sequential generation nature. Conversely, NAR models generate solutions in parallel with a low inference latency but generally exhibit inferior performance. In this paper, we propose a generic Guided Non-Autoregressive Knowledge Distillation (GNARKD) method to obtain high-performance NAR models having a low inference latency. GNARKD removes the constraint of sequential generation in AR models while preserving the learned pivotal components in the network architecture to obtain the corresponding NAR models through knowledge distillation. We evaluate GNARKD by applying it to three widely adopted AR models to obtain NAR VRP solvers for both synthesized and real-world instances. The experimental results demonstrate that GNARKD significantly reduces the inference time (4-5 times faster) with acceptable performance drop (2-3\%). To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.
The task of multimodal relation extraction has attracted significant research attention, but progress is constrained by the scarcity of available training data. One natural thought is to extend existing datasets with cross-modal generative models. In this paper, we consider a novel problem setting, where only unimodal data, either text or image, are available during training. We aim to train a multimodal classifier from synthetic data that perform well on real multimodal test data. However, training with synthetic data suffers from two obstacles: lack of data diversity and label information loss. To alleviate the issues, we propose Mutual Information-aware Multimodal Iterated Relational dAta GEneration (MI2RAGE), which applies Chained Cross-modal Generation (CCG) to promote diversity in the generated data and exploits a teacher network to select valuable training samples with high mutual information with the ground-truth labels. Comparing our method to direct training on synthetic data, we observed a significant improvement of 24.06% F1 with synthetic text and 26.42% F1 with synthetic images. Notably, our best model trained on completely synthetic images outperforms prior state-of-the-art models trained on real multimodal data by a margin of 3.76% in F1. Our codebase will be made available upon acceptance.
From an enormous amount of image-text pairs, large-scale vision-language models (VLMs) learn to implicitly associate image regions with words, which is vital for tasks such as image captioning and visual question answering. However, leveraging such pre-trained models for open-vocabulary semantic segmentation remains a challenge. In this paper, we propose a simple, yet extremely effective, training-free technique, Plug-and-Play Open-Vocabulary Semantic Segmentation (PnP-OVSS) for this task. PnP-OVSS leverages a VLM with direct text-to-image cross-attention and an image-text matching loss to produce semantic segmentation. However, cross-attention alone tends to over-segment, whereas cross-attention plus GradCAM tend to under-segment. To alleviate this issue, we introduce Salience Dropout; by iteratively dropping patches that the model is most attentive to, we are able to better resolve the entire extent of the segmentation mask. Compared to existing techniques, the proposed method does not require any neural network training and performs hyperparameter tuning without the need for any segmentation annotations, even for a validation set. PnP-OVSS demonstrates substantial improvements over a comparable baseline (+29.4% mIoU on Pascal VOC, +13.2% mIoU on Pascal Context, +14.0% mIoU on MS COCO, +2.4% mIoU on COCO Stuff) and even outperforms most baselines that conduct additional network training on top of pretrained VLMs.
Psychological research suggests the central role of event causality in human story understanding. Further, event causality has been heavily utilized in symbolic story generation. However, few machine learning systems for story understanding employ event causality, partially due to the lack of reliable methods for identifying open-world causal event relations. Leveraging recent progress in large language models (LLMs), we present the first method for event causality identification that leads to material improvements in computational story understanding. We design specific prompts for extracting event causal relations from GPT. Against human-annotated event causal relations in the GLUCOSE dataset, our technique performs on par with supervised models, while being easily generalizable to stories of different types and lengths. The extracted causal relations lead to 5.7\% improvements on story quality evaluation and 8.7\% on story video-text alignment. Our findings indicate enormous untapped potential for event causality in computational story understanding.
This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s. one-to-one manners during the training and inference phases, respectively. We argue that this discrepancy arises from the lack of elaborate supervision for each group token. To bridge this granularity gap, this paper explores explicit supervision for the group tokens from the prototypical knowledge. To this end, this paper proposes the non-learnable prototypical regularization (NPR) where non-learnable prototypes are estimated from source features to serve as supervision and enable contrastive matching of the group tokens. This regularization encourages the group tokens to segment objects with less redundancy and capture more comprehensive semantic regions, leading to increased compactness and richness. Based on NPR, we propose the prototypical guidance segmentation network (PGSeg) that incorporates multi-modal regularization by leveraging prototypical sources from both images and texts at different levels, progressively enhancing the segmentation capability with diverse prototypical patterns. Experimental results show that our proposed method achieves state-of-the-art performance on several benchmark datasets. The source code is available at https://github.com/Ferenas/PGSeg.
Research in explainable AI (XAI) aims to provide insights into the decision-making process of opaque AI models. To date, most XAI methods offer one-off and static explanations, which cannot cater to the diverse backgrounds and understanding levels of users. With this paper, we investigate if free-form conversations can enhance users' comprehension of static explanations, improve acceptance and trust in the explanation methods, and facilitate human-AI collaboration. Participants are presented with static explanations, followed by a conversation with a human expert regarding the explanations. We measure the effect of the conversation on participants' ability to choose, from three machine learning models, the most accurate one based on explanations and their self-reported comprehension, acceptance, and trust. Empirical results show that conversations significantly improve comprehension, acceptance, trust, and collaboration. Our findings highlight the importance of customized model explanations in the format of free-form conversations and provide insights for the future design of conversational explanations.