Search Results for author:
Found 17 papers, 7 papers with code
Do LVLMs Understand Charts? Analyzing and Correcting Factual Errors in Chart Captioning
This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation mechanism, and demonstrating an effective approach to ensuring the factuality of generated chart captions.
Factual Inconsistency Detection in Chart Captioning
Image Captioning
+1
Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropout
Spatial-temporal (ST) graph modeling, such as traffic speed forecasting and taxi demand prediction, is an important task in deep learning area.
Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting
This task is challenging due to the complicated spatiotemporal dependencies among regions.
Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network
Predicting Origin-Destination (OD) flow is a crucial problem for intelligent transportation.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
To incorporate multiple relationships into spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks.
Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused.
Predicting Individual Treatment Effects of Large-scale Team Competitions in a Ride-sharing Economy
Through interpreting the best-performing models, we discover many novel and actionable insights regarding how to optimize the design and the execution of team competitions on ride-sharing platforms.
An Intelligent Model for Solving Manpower Scheduling Problems
In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results.
Apply Artificial Neural Network to Solving Manpower Scheduling Problem
This paper proposes a new model combined with deep learning to solve the multi-shift manpower scheduling problem based on the existing research.
Research on the Inverse Kinematics Prediction of a Soft Biomimetic Actuator via BP Neural Network
After training with sample data, the BP neural network model can represent the relation between the manipulator tip position and the pressure applied to the chambers.
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction
Deep learning based approaches have been widely used in various urban spatio-temporal forecasting problems, but most of them fail to account for the unsmoothness issue of urban data in their architecture design, which significantly deteriorates their prediction performance.
Robust Perception through Equivariance
In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference.
Adversarially Robust Video Perception by Seeing Motion
Even under adaptive attacks where the adversary knows our defense, our algorithm is still effective.
Multitask Weakly Supervised Learning for Origin Destination Travel Time Estimation
Subsequently, a Multitask Weakly Supervised Learning Framework for Travel Time Estimation (MWSL TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously.
Continual Diffusion: Continual Customization of Text-to-Image Diffusion with C-LoRA
We show that C-LoRA not only outperforms several baselines for our proposed setting of text-to-image continual customization, which we refer to as Continual Diffusion, but that we achieve a new state-of-the-art in the well-established rehearsal-free continual learning setting for image classification.
Prompt Tuning for Zero-shot Compositional Learning
In order to achieve this goal, a model has to be "smart" and "knowledgeable".
