Search Results for author:
Found 23 papers, 8 papers with code
Integrating Semantic Scenario and Word Relations for Abstractive Sentence Summarization
Recently graph-based methods have been adopted for Abstractive Text Summarization.
基于语料库的武侠与仙侠网络小说文体、词汇及主题对比分析(A Corpus-based Contrastive Analysis of Style, Vocabulary and Theme of Wuxia and Xianxia Internet Novels)
网络文学在我国发展迅猛, 其数量和影响力呈现逐年上升的趋势, 但目前尚无公开的较大规模网络文学作品语料库, 鲜见基于语料库对网络文学具体类别作品的定量研究。本文初步建立了一个网络文学语料库, 其中包括武侠和仙侠网络小说, 使用文本计量、词频统计以及主题挖掘的方法对两类小说的文体风格、具体词汇使用和小说主题进行对比分析。通过比较, 我们发现两类小说的文体风格大致相同, 它们在词汇的使用和主题上既有共性又各具特色。从微观到宏观, 从表面到内容, 将定量统计和定性分析相结合, 多角度、多层次的对武侠和仙侠网络小说进行比较。
VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation
Given the higher information load processed by large vision-language models (LVLMs) compared to single-modal LLMs, detecting LVLM hallucinations requires more human and time expense, and thus rise a wider safety concerns.
CF-PRNet: Coarse-to-Fine Prototype Refining Network for Point Cloud Completion and Reconstruction
In modern agriculture, precise monitoring of plants and fruits is crucial for tasks such as high-throughput phenotyping and automated harvesting.
Harnessing Uncertainty-aware Bounding Boxes for Unsupervised 3D Object Detection
(2) Based on the assessed uncertainty, we adaptively adjust the weight of every 3D bbox coordinate via uncertainty regularization, refining the training process on pseudo bboxes.
Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene
In this paper, we are among the early attempts to integrate LiDAR data with 2D images for unsupervised 3D detection and introduce a new method, dubbed LiDAR-2D Self-paced Learning (LiSe).
EMBANet: A Flexible Efffcient Multi-branch Attention Network
The proposed MBC module brings new degrees of freedom (DoF) for the design of attention networks by allowing the type of transformation operators and the number of branches to be flexibly adjusted.
OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detection
OpenSight utilizes 2D-3D geometric priors for the initial discernment and localization of generic objects, followed by a more specific semantic interpretation of the detected objects.
Divide and Ensemble: Progressively Learning for the Unknown
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions.
BAVS: Bootstrapping Audio-Visual Segmentation by Integrating Foundation Knowledge
In a nutshell, our BAVS is designed to eliminate the interference of background noise or off-screen sounds in segmentation by establishing the audio-visual correspondences in an explicit manner.
Audio-Visual Segmentation by Exploring Cross-Modal Mutual Semantics
However, we observed that prior arts are prone to segment a certain salient object in a video regardless of the audio information.
Automotive Object Detection via Learning Sparse Events by Spiking Neurons
Traditional object detection techniques that utilize Artificial Neural Networks (ANNs) face challenges due to the sparse and asynchronous nature of the events these sensors capture.
Accurate and Efficient Event-based Semantic Segmentation Using Adaptive Spiking Encoder-Decoder Network
Moreover, we develop a dual-path Spiking Spatially-Adaptive Modulation module, which is specifically tailored to enhance the representation of sparse events and multi-modal inputs, thereby considerably improving network performance.
STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction
High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities.
Physics-informed machine learning
Spatio-Temporal Forecasting
+1
An Evolutionary Forest for Regression
Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years.
A Knowledge-Guided Framework for Frame Identification
In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frame-to-frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings.
EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network
Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it.
Asynchronous Modeling: A Dual-phase Perspective for Long-Tailed Recognition
Motivated by such phenomenon, we propose to disentangle the distinctive effects of data-rich and data-poor gradient and asynchronously train a model via a dual-phase learning process.
Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search
Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability.
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior
Most of previous work on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored.
Query-efficient Meta Attack to Deep Neural Networks
Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries.
Sentence Compression via DC Programming Approach
Sentence compression is an important problem in natural language processing.
Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm
Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run.
