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Found 32 papers, 12 papers with code
Let Real Images be as a Judger, Spotting Fake Images Synthesized with Generative Models
Unfortunately, the artifact patterns in fake images synthesized by different generative models are inconsistent, leading to the failure of previous research that relied on spotting subtle differences between real and fake.
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning
In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
Towards Training A Chinese Large Language Model for Anesthesiology
3) We introduce a standardized benchmark for evaluating medical LLM in Anesthesiology.
Poisson Process for Bayesian Optimization
BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP).
Lips Are Lying: Spotting the Temporal Inconsistency between Audio and Visual in Lip-Syncing DeepFakes
In recent years, DeepFake technology has achieved unprecedented success in high-quality video synthesis, whereas these methods also pose potential and severe security threats to humanity.
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data.
Prompt-Learning for Cross-Lingual Relation Extraction
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
Hybrid Multimodal Fusion for Humor Detection
Our experiments demonstrate the effectiveness of our proposed model and hybrid fusion strategy on multimodal fusion, and the AUC of our proposed model on the test set is 0. 8972.
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding
For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e. g. mBART, the self-supervised pretraining task is trained on a wide range of monolingual languages, e. g. 25 languages from CommonCrawl, while the downstream cross-lingual tasks generally progress on a bilingual language subset, e. g. English-German, making there exists the data discrepancy, namely domain discrepancy, and cross-lingual learning objective discrepancy, namely task discrepancy, between the pretraining and finetuning stages.
Selecting task with optimal transport self-supervised learning for few-shot classification
Few-Shot classification aims at solving problems that only a few samples are available in the training process.
GCT: Graph Co-Training for Semi-Supervised Few-Shot Learning
First, we propose a novel label prediction method, Isolated Graph Learning (IGL).
Where Does the Performance Improvement Come From? -- A Reproducibility Concern about Image-Text Retrieval
In contrast to previous works, we focus on the reproducibility of the approaches and the examination of the elements that lead to improved performance by pretrained and nonpretrained models in retrieving images and text.
MSCET: A Multi-Scenario Offloading Schedule for Biomedical Data Processing and Analysis in Cloud-Edge-Terminal Collaborative Vehicular Networks
The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge.
MDFM: Multi-Decision Fusing Model for Few-Shot Learning
Inspired by this assumption, we propose a novel method Multi-Decision Fusing Model (MDFM), which comprehensively considers the decisions based on multiple FEMs to enhance the efficacy and robustness of the model.
How Frequency Effect Graph Neural Networks
Based on the frequency principle on GNNs, we present a novel powerful GNNs framework, Multi-Scale Frequency Enhanced Graph Neural Networks (MSF-GNNs) which considers multi-scale representations from wavelet decomposition.
Semantic-Preserving Adversarial Text Attacks
In this paper, we propose a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) method to examine the vulnerability of deep models.
Hazy Re-ID: An Interference Suppression Model For Domain Adaptation Person Re-identification Under Inclement Weather Condition
This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID.
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach
However, recent studies have pointed out that the highlighted image regions from the visual attention are often irrelevant to the given question and answer, leading to model confusion for correct visual reasoning.
SAHDL: Sparse Attention Hypergraph Regularized Dictionary Learning
In this paper, we propose a hypergraph based sparse attention mechanism to tackle this issue and embed it into dictionary learning.
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
To alleviate these problems, this paper proposes the targeted attention attack (TAA) method for real world road sign attack.
Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples
Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain.
Person Re-Identification via Active Hard Sample Mining
To alleviate such a problem, we present an active hard sample mining framework via training an effective re-ID model with the least labeling efforts.
Multiview Hessian Regularization for Image Annotation
One representative work in SSL is Laplacian regularization (LR), which smoothes the conditional distribution for classification along the manifold encoded in the graph Laplacian, however, it has been observed that LR biases the classification function towards a constant function which possibly results in poor generalization.
Label Embedded Dictionary Learning for Image Classification
Recently, label consistent k-svd (LC-KSVD) algorithm has been successfully applied in image classification.
Ensemble p-Laplacian Regularization for Remote Sensing Image Recognition
Therefore, we develop an ensemble p-Laplacian regularization (EpLapR) to fully approximate the intrinsic manifold of the data distribution.
Hypergraph p-Laplacian Regularization for Remote Sensing Image Recognition
Graph based SSL and manifold regularization based SSL including Laplacian regularization (LapR) and Hypergraph Laplacian regularization (HLapR) are representative SSL methods and have achieved prominent performance by exploiting the relationship of sample distribution.
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition
Since human actions can be characterized by multiple feature representations extracted from Kinect and inertial sensors, multiview features must be encoded into a unified space optimal for human action recognition.
Multi-view Face Analysis Based on Gabor Features
Different methods of classification based on sparse representation and Gabor kernels have been widely applied in the fields of facial analysis.
Multiview Hessian Discriminative Sparse Coding for Image Annotation
In particular, mHDSC exploits Hessian regularization to steer the solution which varies smoothly along geodesics in the manifold, and treats the label information as an additional view of feature for incorporating the discriminative power for image annotation.
Dictionary learning based image enhancement for rarity detection
Image enhancement is an important image processing technique that processes images suitably for a specific application e. g. image editing.
