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Found 60 papers, 23 papers with code
多模态表述视域下的小学数学课堂语言计量初探(A preliminary study of language measurement in elementary school mathematics classrooms from the perspective of multimodal representation)
“本文重点探讨小学数学课堂多模态话语的分析和计量。本文以一堂数学优质课为语料, 探讨多模态语料库的加工标注, 提出两种多模态语言计量方法:多模态值和多模态表征离散程度, 并对量化的多模态语言抽样数据结果进行分析。研究发现:教师能够借助多模态语言更好的传递抽象知识, 计量结果能够反映模态之间的协同表述关系, 以及课堂教学的多模态语言演绎是否恰当。”
InternLM2 Technical Report
The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI).
Ranked #5 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Breaking the Barrier: Utilizing Large Language Models for Industrial Recommendation Systems through an Inferential Knowledge Graph
In summary, LLM-KERec addresses the limitations of traditional recommendation systems by incorporating complementary knowledge and utilizing a large language model to capture user intent transitions, adapt to new items, and enhance recommendation efficiency in the evolving e-commerce landscape.
The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy.
MDGNN: Multi-Relational Dynamic Graph Neural Network for Comprehensive and Dynamic Stock Investment Prediction
The stock market is a crucial component of the financial system, but predicting the movement of stock prices is challenging due to the dynamic and intricate relations arising from various aspects such as economic indicators, financial reports, global news, and investor sentiment.
Guided Image Restoration via Simultaneous Feature and Image Guided Fusion
Currently, joint image filtering-inspired deep learning-based methods represent the state-of-the-art for GIR tasks.
Long-tail Augmented Graph Contrastive Learning for Recommendation
To make the data augmentation schema learnable, we design an auto drop module to generate pseudo-tail nodes from head nodes and a knowledge transfer module to reconstruct the head nodes from pseudo-tail nodes.
Leveraging Contextual Information for Effective Entity Salience Detection
In text documents such as news articles, the content and key events usually revolve around a subset of all the entities mentioned in a document.
MultiModal-GPT: A Vision and Language Model for Dialogue with Humans
To further enhance the ability to chat with humans of the MultiModal-GPT, we utilize language-only instruction-following data to train the MultiModal-GPT jointly.
Interactive Segmentation as Gaussian Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
Interactive Segmentation As Gaussion Process Classification
Against this issue, in this paper, we propose to formulate the IS task as a Gaussian process (GP)-based pixel-wise binary classification model on each image.
A Hyper-weight Network for Hyperspectral Image Denoising
Extensive experiments verify that the proposed HWnet can help improve the generalization ability of a weighted model to adapt to more complex noise, and can also strengthen the weighted model by transferring the knowledge from another weighted model.
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images.
An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022
Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area.
Diagnosing Batch Normalization in Class Incremental Learning
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL).
Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment
Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance.
ECNU\_ICA\_1 SemEval-2021 Task 4: Leveraging Knowledge-enhanced Graph Attention Networks for Reading Comprehension of Abstract Meaning
This paper describes our system for SemEval-2021 Task 4: Reading Comprehension of Abstract Meaning.
Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
It has been shown that equivariant convolution is very helpful for many types of computer vision tasks.
RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining
To handle such an ill-posed single image deraining task, in this paper, we specifically build a novel deep architecture, called rain convolutional dictionary network (RCDNet), which embeds the intrinsic priors of rain streaks and has clear interpretability.
Blind Image Super-resolution with Elaborate Degradation Modeling on Noise and Kernel
To address the above issues, this paper proposes a model-based blind SISR method under the probabilistic framework, which elaborately models image degradation from the perspectives of noise and blur kernel.
Learning an Explicit Weighting Scheme for Adapting Complex HSI Noise
A general approach for handling hyperspectral image (HSI) denoising issue is to impose weights on different HSI pixels to suppress negative influence brought by noisy elements.
A Deep Variational Bayesian Framework for Blind Image Deblurring
Under this framework, the posterior of the latent clean image and blur kernel can be jointly estimated in an amortized inference fashion with DNNs, and the involved inference DNNs can be trained by fully considering the physical blur model, together with the supervision of data driven priors for the clean image and blur kernel, which is naturally led to by the evidence lower bound objective.
Semi-Supervised Video Deraining with Dynamical Rain Generator
Firstly, most of them do not sufficiently model the characteristics of rain layers of rainy videos.
Depth Evaluation for Metal Surface Defects by Eddy Current Testing using Deep Residual Convolutional Neural Networks
Eddy current testing (ECT) is an effective technique in the evaluation of the depth of metal surface defects.
Cross-Supervised Joint-Event-Extraction with Heterogeneous Information Networks
To verify the effectiveness of our proposed method, we conduct extensive experiments on four real-world datasets as well as compare our method with state-of-the-art methods.
Deep Variational Network Toward Blind Image Restoration
In this proposed model, a pixel-wise non-i. i. d.
Meta Feature Modulator for Long-tailed Recognition
We further design a modulator network to guide the generation of the modulation parameters, and such a meta-learner can be readily adapted to train the classification network on other long-tailed datasets.
From Rain Generation to Rain Removal
For the single image rain removal (SIRR) task, the performance of deep learning (DL)-based methods is mainly affected by the designed deraining models and training datasets.
Learning to Purify Noisy Labels via Meta Soft Label Corrector
By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, we could adaptively obtain rectified soft labels iteratively according to current training problems without manually preset hyper-parameters.
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Meanwhile, it always needs to search proper LR schedules from scratch for new tasks, which, however, are often largely different with task variations, like data modalities, network architectures, or training data capacities.
ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation
As a result, this model performs quite well in both validation and explanation.
Dual Adversarial Network: Toward Real-world Noise Removal and Noise Generation
Specifically, we approximate the joint distribution with two different factorized forms, which can be formulated as a denoiser mapping the noisy image to the clean one and a generator mapping the clean image to the noisy one.
Ranked #2 on
Noise Estimation
on SIDD
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted labels.
Structural Residual Learning for Single Image Rain Removal
Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages.
A Model-driven Deep Neural Network for Single Image Rain Removal
Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model.
Learning Adaptive Loss for Robust Learning with Noisy Labels
Four kinds of SOTA robust loss functions are attempted to be integrated into our algorithm, and comprehensive experiments substantiate the general availability and effectiveness of the proposed method in both its accuracy and generalization performance, as compared with conventional hyperparameter tuning strategy, even with carefully tuned hyperparameters.
Neural Networks Weights Quantization: Target None-retraining Ternary (TNT)
Quantization of weights of deep neural networks (DNN) has proven to be an effective solution for the purpose of implementing DNNs on edge devices such as mobiles, ASICs and FPGAs, because they have no sufficient resources to support computation involving millions of high precision weights and multiply-accumulate operations.
Understand Dynamic Regret with Switching Cost for Online Decision Making
We provide a new theoretical analysis framework, which shows an interesting observation, that is, the relation between the switching cost and the dynamic regret is different for settings of OA and OCO.
A Survey on Rain Removal from Video and Single Image
The investigations on rain removal from video or a single image has thus been attracting much research attention in the field of computer vision and pattern recognition, and various methods have been proposed against this task in the recent years.
Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Furthermore, a transformation operator imposed on the background scenes is further embedded into the proposed model, which finely conveys the dynamic background transformations, such as rotations, scalings and distortions, inevitably existed in a real video sequence.
Variational Denoising Network: Toward Blind Noise Modeling and Removal
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression.
Ranked #10 on
Image Denoising
on DND
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance.
Ranked #24 on
Image Classification
on Clothing1M
(using extra training data)
Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net
In this paper, we propose a model-based deep learning approach for merging an HrMS and LrHS images to generate a high-resolution hyperspectral (HrHS) image.
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing
The 3-D total variation (3DTV) is a powerful regularization term, which encodes the local smoothness prior structure underlying a hyper-spectral image (HSI), for general HSI processing tasks.
Discovering Influential Factors in Variational Autoencoder
In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks.
Unsupervised/Semi-supervised Deep Learning for Low-dose CT Enhancement
To address this issue, we propose an unsupervised DL method for LdCT enhancement that incorporates unlabeled LdCT sinograms directly into the network training.
Semi-supervised Transfer Learning for Image Rain Removal
However, previous deep learning methods need to pre-collect a large set of image pairs with/without synthesized rain for training, which tends to make the neural network be biased toward learning the specific patterns of the synthesized rain, while be less able to generalize to real test samples whose rain types differ from those in the training data.
Ranked #8 on
Single Image Deraining
on Test100
Video Rain Streak Removal by Multiscale Convolutional Sparse Coding
Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.
Preliminary theoretical troubleshooting in Variational Autoencoder
This paper tries to preliminarily address VAE's intrinsic dimension, real factor, disentanglement and indicator issues theoretically in the idealistic situation and implementation issue practically through noise modeling perspective in the realistic case.
Should We Encode Rain Streaks in Video as Deterministic or Stochastic?
Videos taken in the wild sometimes contain unexpected rain streaks, which brings difficulty in subsequent video processing tasks.
Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition
In this paper, we present a novel tensor-based HSI restoration approach by fully identifying the intrinsic structures of the clean HSI part and the mixed noise part respectively.
SPLBoost: An Improved Robust Boosting Algorithm Based on Self-paced Learning
Specifically, the underlying loss being minimized by the traditional AdaBoost is the exponential loss, which is proved to be very sensitive to random noise/outliers.
A General Model for Robust Tensor Factorization with Unknown Noise
We provide two versions of the algorithm with different tensor factorization operations, i. e., CP factorization and Tucker factorization.
Denoising Hyperspectral Image with Non-i.i.d. Noise Structure
In real scenarios, however, the noise existed in a natural HSI is always with much more complicated non-i. i. d.
Robust Tensor Factorization With Unknown Noise
However, real data are often corrupted by noise with an unknown distribution.
Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization
Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.
Low-rank Matrix Factorization under General Mixture Noise Distributions
Many computer vision problems can be posed as learning a low-dimensional subspace from high dimensional data.
A Self-Paced Multiple-Instance Learning Framework for Co-Saliency Detection
As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects in a group of images.
A Novel Sparsity Measure for Tensor Recovery
In this paper, we propose a new sparsity regularizer for measuring the low-rank structure underneath a tensor.
What Objective Does Self-paced Learning Indeed Optimize?
Self-paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals.
