Traj-Explainer: An Explainable and Robust Multi-modal Trajectory Prediction Approach

Navigating complex traffic environments has been significantly enhanced by advancements in intelligent technologies, enabling accurate environment perception and trajectory prediction for automated vehicles. However, existing research often neglects the consideration of the joint reasoning of scenario agents and lacks interpretability in trajectory prediction models, thereby limiting their practical application in real-world scenarios. To this purpose, an explainability-oriented trajectory prediction model is designed in this work, named Explainable Conditional Diffusion based Multimodal Trajectory Prediction Traj-Explainer, to retrieve the influencing factors of prediction and help understand the intrinsic mechanism of prediction. In Traj-Explainer, a modified conditional diffusion is well designed to capture the scenario multimodal trajectory pattern, and meanwhile, a modified Shapley Value model is assembled to rationally learn the importance of the global and scenario features. Numerical experiments are carried out by several trajectory prediction datasets, including Waymo, NGSIM, HighD, and MoCAD datasets. Furthermore, we evaluate the identified input factors which indicates that they are in agreement with the human driving experience, indicating the capability of the proposed model in appropriately learning the prediction. Code available in our open-source repository: \url{https://anonymous.4open.science/r/Interpretable-Prediction}.

HeightFormer: A Semantic Alignment Monocular 3D Object Detection Method from Roadside Perspective

The on-board 3D object detection technology has received extensive attention as a critical technology for autonomous driving, while few studies have focused on applying roadside sensors in 3D traffic object detection. Existing studies achieve the projection of 2D image features to 3D features through height estimation based on the frustum. However, they did not consider the height alignment and the extraction efficiency of bird's-eye-view features. We propose a novel 3D object detection framework integrating Spatial Former and Voxel Pooling Former to enhance 2D-to-3D projection based on height estimation. Extensive experiments were conducted using the Rope3D and DAIR-V2X-I dataset, and the results demonstrated the outperformance of the proposed algorithm in the detection of both vehicles and cyclists. These results indicate that the algorithm is robust and generalized under various detection scenarios. Improving the accuracy of 3D object detection on the roadside is conducive to building a safe and trustworthy intelligent transportation system of vehicle-road coordination and promoting the large-scale application of autonomous driving. The code and pre-trained models will be released on https://anonymous.4open.science/r/HeightFormer.

HierLLM: Hierarchical Large Language Model for Question Recommendation

Question recommendation is a task that sequentially recommends questions for students to enhance their learning efficiency. That is, given the learning history and learning target of a student, a question recommender is supposed to select the question that will bring the most improvement for students. Previous methods typically model the question recommendation as a sequential decision-making problem, estimating students' learning state with the learning history, and feeding the learning state with the learning target to a neural network to select the recommended question from a question set. However, previous methods are faced with two challenges: (1) learning history is unavailable in the cold start scenario, which makes the recommender generate inappropriate recommendations; (2) the size of the question set is much large, which makes it difficult for the recommender to select the best question precisely. To address the challenges, we propose a method called hierarchical large language model for question recommendation (HierLLM), which is a LLM-based hierarchical structure. The LLM-based structure enables HierLLM to tackle the cold start issue with the strong reasoning abilities of LLM. The hierarchical structure takes advantage of the fact that the number of concepts is significantly smaller than the number of questions, narrowing the range of selectable questions by first identifying the relevant concept for the to-recommend question, and then selecting the recommended question based on that concept. This hierarchical structure reduces the difficulty of the recommendation.To investigate the performance of HierLLM, we conduct extensive experiments, and the results demonstrate the outstanding performance of HierLLM.

Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance

Incomplete multi-view clustering primarily focuses on dividing unlabeled data into corresponding categories with missing instances, and has received intensive attention due to its superiority in real applications. Considering the influence of incomplete data, the existing methods mostly attempt to recover data by adding extra terms. However, for the unsupervised methods, a simple recovery strategy will cause errors and outlying value accumulations, which will affect the performance of the methods. Broadly, the previous methods have not taken the effectiveness of recovered instances into consideration, or cannot flexibly balance the discrepancies between recovered data and original data. To address these problems, we propose a novel method termed Manifold-based Incomplete Multi-view clustering via Bi-consistency guidance (MIMB), which flexibly recovers incomplete data among various views, and attempts to achieve biconsistency guidance via reverse regularization. In particular, MIMB adds reconstruction terms to representation learning by recovering missing instances, which dynamically examines the latent consensus representation. Moreover, to preserve the consistency information among multiple views, MIMB implements a biconsistency guidance strategy with reverse regularization of the consensus representation and proposes a manifold embedding measure for exploring the hidden structure of the recovered data. Notably, MIMB aims to balance the importance of different views, and introduces an adaptive weight term for each view. Finally, an optimization algorithm with an alternating iteration optimization strategy is designed for final clustering. Extensive experimental results on 6 benchmark datasets are provided to confirm that MIMB can significantly obtain superior results as compared with several state-of-the-art baselines.

Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting

Denoising diffusion probabilistic models for image inpainting aim to add the noise to the texture of image during the forward process and recover masked regions with unmasked ones of the texture via the reverse denoising process.Despite the meaningful semantics generation,the existing arts suffer from the semantic discrepancy between masked and unmasked regions, since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process,leading to the large discrepancy between them. In this paper,we aim to answer how unmasked semantics guide texture denoising process;together with how to tackle the semantic discrepancy,to facilitate the consistent and meaningful semantics generation. To this end,we propose a novel structure-guided diffusion model named StrDiffusion,to reformulate the conventional texture denoising process under structure guidance to derive a simplified denoising objective for image inpainting,while revealing:1)the semantically sparse structure is beneficial to tackle semantic discrepancy in early stage, while dense texture generates reasonable semantics in late stage;2)the semantics from unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process,benefiting from the time-dependent sparsity of the structure semantics.For the denoising process,a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions.Besides,we devise an adaptive resampling strategy as a formal criterion as whether structure is competent to guide the texture denoising process,while regulate their semantic correlations.Extensive experiments validate the merits of StrDiffusion over the state-of-the-arts.Our code is available at https://github.com/htyjers/StrDiffusion.

Fine-grained Cross-modal Fusion based Refinement for Text-to-Image Synthesis

Text-to-image synthesis refers to generating visual-realistic and semantically consistent images from given textual descriptions. Previous approaches generate an initial low-resolution image and then refine it to be high-resolution. Despite the remarkable progress, these methods are limited in fully utilizing the given texts and could generate text-mismatched images, especially when the text description is complex. We propose a novel Fine-grained text-image Fusion based Generative Adversarial Networks, dubbed FF-GAN, which consists of two modules: Fine-grained text-image Fusion Block (FF-Block) and Global Semantic Refinement (GSR). The proposed FF-Block integrates an attention block and several convolution layers to effectively fuse the fine-grained word-context features into the corresponding visual features, in which the text information is fully used to refine the initial image with more details. And the GSR is proposed to improve the global semantic consistency between linguistic and visual features during the refinement process. Extensive experiments on CUB-200 and COCO datasets demonstrate the superiority of FF-GAN over other state-of-the-art approaches in generating images with semantic consistency to the given texts.Code is available at https://github.com/haoranhfut/FF-GAN.

Deep Cost-sensitive Learning for Wheat Frost Detection

Frost damage is one of the main factors leading to wheat yield reduction. Therefore, the detection of wheat frost accurately and efficiently is beneficial for growers to take corresponding measures in time to reduce economic loss. To detect the wheat frost, in this paper we create a hyperspectral wheat frost data set by collecting the data characterized by temperature, wheat yield, and hyperspectral information provided by the handheld hyperspectral spectrometer. However, due to the imbalance of data, that is, the number of healthy samples is much higher than the number of frost damage samples, a deep learning algorithm tends to predict biasedly towards the healthy samples resulting in model overfitting of the healthy samples. Therefore, we propose a method based on deep cost-sensitive learning, which uses a one-dimensional convolutional neural network as the basic framework and incorporates cost-sensitive learning with fixed factors and adjustment factors into the loss function to train the network. Meanwhile, the accuracy and score are used as evaluation metrics. Experimental results show that the detection accuracy and the score reached 0.943 and 0.623 respectively, this demonstration shows that this method not only ensures the overall accuracy but also effectively improves the detection rate of frost samples.

Delving Globally into Texture and Structure for Image Inpainting

Image inpainting has achieved remarkable progress and inspired abundant methods, where the critical bottleneck is identified as how to fulfill the high-frequency structure and low-frequency texture information on the masked regions with semantics. To this end, deep models exhibit powerful superiority to capture them, yet constrained on the local spatial regions. In this paper, we delve globally into texture and structure information to well capture the semantics for image inpainting. As opposed to the existing arts trapped on the independent local patches, the texture information of each patch is reconstructed from all other patches across the whole image, to match the coarsely filled information, specially the structure information over the masked regions. Unlike the current decoder-only transformer within the pixel level for image inpainting, our model adopts the transformer pipeline paired with both encoder and decoder. On one hand, the encoder captures the texture semantic correlations of all patches across image via self-attention module. On the other hand, an adaptive patch vocabulary is dynamically established in the decoder for the filled patches over the masked regions. Building on this, a structure-texture matching attention module anchored on the known regions comes up to marry the best of these two worlds for progressive inpainting via a probabilistic diffusion process. Our model is orthogonal to the fashionable arts, such as Convolutional Neural Networks (CNNs), Attention and Transformer model, from the perspective of texture and structure information for image inpainting. The extensive experiments over the benchmarks validate its superiority. Our code is available at https://github.com/htyjers/DGTS-Inpainting.