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Found 34 papers, 6 papers with code
Towards Enhancing Faithfulness for Neural Machine Translation
Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences.
CoGS: Controllable Gaussian Splatting
We present CoGS, a method for Controllable Gaussian Splatting, that enables the direct manipulation of scene elements, offering real-time control of dynamic scenes without the prerequisite of pre-computing control signals.
SubZero: Subspace Zero-Shot MRI Reconstruction
Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high-quality reconstructions without access to a large training dataset.
Instruct2Attack: Language-Guided Semantic Adversarial Attacks
We propose Instruct2Attack (I2A), a language-guided semantic attack that generates semantically meaningful perturbations according to free-form language instructions.
TFDet: Target-Aware Fusion for RGB-T Pedestrian Detection
In this paper, we propose a novel target-aware fusion strategy for multispectral pedestrian detection, named TFDet.
Unsupervised Style-based Explicit 3D Face Reconstruction from Single Image
Inferring 3D object structures from a single image is an ill-posed task due to depth ambiguity and occlusion.
DyLiN: Making Light Field Networks Dynamic
Light Field Networks, the re-formulations of radiance fields to oriented rays, are magnitudes faster than their coordinate network counterparts, and provide higher fidelity with respect to representing 3D structures from 2D observations.
Patch Network for medical image Segmentation
Accurate and fast segmentation of medical images is clinically essential, yet current research methods include convolutional neural networks with fast inference speed but difficulty in learning image contextual features, and transformer with good performance but high hardware requirements.
Non-pooling Network for medical image segmentation
Existing studies tend tofocus onmodel modifications and integration with higher accuracy, which improve performance but also carry huge computational costs, resulting in longer detection times.
CoNFies: Controllable Neural Face Avatars
Neural Radiance Fields (NeRF) are compelling techniques for modeling dynamic 3D scenes from 2D image collections.
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation
Although data augmentation is widely used to enrich the training data, conventional methods with discrete manipulations fail to generate diverse and faithful training samples.
GPU-Net: Lightweight U-Net with more diverse features
Image segmentation is an important task in the medical image field and many convolutional neural networks (CNNs) based methods have been proposed, among which U-Net and its variants show promising performance.
Dynamic Texture Recognition using PDV Hashing and Dictionary Learning on Multi-scale Volume Local Binary Pattern
To tackle this problem, we propose a method for dynamic texture recognition using PDV hashing and dictionary learning on multi-scale volume local binary pattern (PHD-MVLBP).
eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction
RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan.
Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
Conclusion: SPARK synergizes with physics-based acquisition and reconstruction techniques to improve accelerated MRI by training scan-specific models to estimate and correct reconstruction errors in k-space.
Translation Memory Guided Neural Machine Translation
Existing ways either employ extra encoder to encode information from TM or concatenate source sentence and TM sentences as encoder's input.
Factorized Transformer for Multi-Domain Neural Machine Translation
Multi-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains.
Uncertainty-Aware Semantic Augmentation for Neural Machine Translation
As a sequence-to-sequence generation task, neural machine translation (NMT) naturally contains intrinsic uncertainty, where a single sentence in one language has multiple valid counterparts in the other.
On Learning Universal Representations Across Languages
Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks.
Contrastive Learning
Cross-Lingual Natural Language Inference
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Language-aware Interlingua for Multilingual Neural Machine Translation
However, the traditional multilingual model fails to capture the diversity and specificity of different languages, resulting in inferior performance compared with individual models that are sufficiently trained.
Multiscale Collaborative Deep Models for Neural Machine Translation
Recent evidence reveals that Neural Machine Translation (NMT) models with deeper neural networks can be more effective but are difficult to train.
AR: Auto-Repair the Synthetic Data for Neural Machine Translation
Compared with only using limited authentic parallel data as training corpus, many studies have proved that incorporating synthetic parallel data, which generated by back translation (BT) or forward translation (FT, or selftraining), into the NMT training process can significantly improve translation quality.
GRET: Global Representation Enhanced Transformer
The encoder maps the words in the input sentence into a sequence of hidden states, which are then fed into the decoder to generate the output sentence.
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation
The standard paradigm of exploiting them includes two steps: first, pre-training a model, e. g. BERT, with a large scale unlabeled monolingual data.
Improving Neural Machine Translation with Pre-trained Representation
Then, we design a framework for integrating both source and target sentence-level representations into NMT model to improve the translation quality.
Sequence Generation: From Both Sides to the Middle
The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization.
Code-Switching for Enhancing NMT with Pre-Specified Translation
Leveraging user-provided translation to constrain NMT has practical significance.
Improving Multilingual Semantic Textual Similarity with Shared Sentence Encoder for Low-resource Languages
Our approach is extended from a basic monolingual STS framework to a shared multilingual encoder pretrained with translation task to incorporate rich-resource language data.
Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora.
