Therefore, in this paper, we are the first to jointly perform multi-modal ATE (MATE) and multi-modal ASC (MASC), and we propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-modal aspect-level sentiment analysis (MALSA).
This simplifies the domain adaption from generic to specific scenes during model reasoning processes.
We also show that pretraining the QE module with auto-generated QE data from user logs can further improve the overall performance.
It can learn efficient representations from both cell-structured networks and entire networks.
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
Furthermore, we propose a pre-trained model to integrate both syntax and semantic features for opinion tree generation.
Attention mechanism has become the dominant module in natural language processing models.
In this technical report, we present our 1st place solution for the ICDAR 2021 competition on mathematical formula detection (MFD).
Previous studies show effective of pre-trained language models for sentiment analysis.
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
This paper presents our solution for the ICDAR 2021 Competition on Scientific Table Image Recognition to LaTeX.
In our method, we divide the table content recognition task into foursub-tasks: table structure recognition, text line detection, text line recognition, and box assignment. Our table structure recognition algorithm is customized based on MASTER , a robust image textrecognition algorithm.
Ranked #1 on Table Recognition on PubTabNet
We propose a novel way to consider samples of different relevance confidence, and come up with a new training objective to learn a robust relevance model with desirable score distribution.
We conduct extensive experiments on benchmark datasets for different tasks, including node classification, link prediction, graph classification and graph regression, and confirm that the learned graph normalization leads to competitive results and that the learned weights suggest the appropriate normalization techniques for the specific task.
Thus, we propose a lightweight scene text recognition model named Hamming OCR.
Given two relevant domains (e. g., Book and Movie), users may have interactions with items in one domain but not in the other domain.
Most of ranking models are trained only with displayed items (most are hot items), but they are utilized to retrieve items in the entire space which consists of both displayed and non-displayed items (most are long-tail items).
Computer vision with state-of-the-art deep learning models has achieved huge success in the field of Optical Character Recognition (OCR) including text detection and recognition tasks recently.
We utilise the overlay between the accurate mask prediction and less accurate mesh prediction to iteratively optimise the direct regressed 6D pose information with a focus on translation estimation.
Attention-based scene text recognizers have gained huge success, which leverages a more compact intermediate representation to learn 1d- or 2d- attention by a RNN-based encoder-decoder architecture.
We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems.
We study in this paper how to initialize the parameters of multinomial logistic regression (a fully connected layer followed with softmax and cross entropy loss), which is widely used in deep neural network (DNN) models for classification problems.