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Found 536 papers, 173 papers with code
PALM: Pre-training an Autoencoding\&Autoregressive Language Model for Context-conditioned Generation
An extensive set of experiments show that PALM achieves new state-of-the-art results on a variety of language generation benchmarks covering generative question answering (Rank 1 on the official MARCO leaderboard), abstractive summarization on CNN/DailyMail as well as Gigaword, question generation on SQuAD, and conversational response generation on Cornell Movie Dialogues.
Abstractive Text Summarization
Conversational Response Generation
+8
A Bayesian Topic Model for Human-Evaluated Interpretability
Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model.
Robust Graph Representation Learning via Neural Sparsification
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
Improving Empathetic Response Generation by Recognizing Emotion Cause in Conversations
The emotion cause is a stimulus for human emotions.
Qwen Technical Report
Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans.
High-content stimulated Raman histology of human breast cancer
Here, we present a high-content stimulated Raman histology (HC-SRH) platform that provides both morphological and chemical information for cancer diagnosis based on un-stained breast tissues.
Neural Image Compression Using Masked Sparse Visual Representation
A set of semantic-class-dependent basis codebooks are learned, which are weighted combined to generate a rich latent feature for high-quality reconstruction.
Robust Backdoor Attacks on Object Detection in Real World
However, these models are vulnerable to backdoor attacks.
Unveiling Invariances via Neural Network Pruning
Invariance describes transformations that do not alter data's underlying semantics.
A DRL-based Reflection Enhancement Method for RIS-assisted Multi-receiver Communications
In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, the pointing accuracy and intensity of reflections depend crucially on the 'profile,' representing the amplitude/phase state information of all elements in a RIS array.
Mean Field Game-based Waveform Precoding Design for Mobile Crowd Integrated Sensing, Communication, and Computation Systems
In this paper, we consider the environment sensing problem in the large-scale mobile crowd ISCC systems and propose an efficient waveform precoding design algorithm based on the mean field game~(MFG).
MathAttack: Attacking Large Language Models Towards Math Solving Ability
Instead of attacking prompts in the use of LLMs, we propose a MathAttack model to attack MWP samples which are closer to the essence of security in solving math problems.
DALNet: A Rail Detection Network Based on Dynamic Anchor Line
In the paper, motivated by the anchor line-based lane detection methods, we propose a rail detection network called DALNet based on dynamic anchor line.
Multi-scale Target-Aware Framework for Constrained Image Splicing Detection and Localization
Additionally, in order to handle scale transformations, we introduce a multi-scale projection method, which can be readily integrated into our target-aware framework that enables the attention process to be conducted between tokens containing information of varying scales.
Artificial Intelligence for Web 3.0: A Comprehensive Survey
Then, we thoroughly analyze the current state of AI technology applications in the four layers of Web 3. 0 and offer some insights into its potential future development direction.
D-IF: Uncertainty-aware Human Digitization via Implicit Distribution Field
In this paper, we propose replacing the implicit value with an adaptive uncertainty distribution, to differentiate between points based on their distance to the surface.
KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases
Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability.
Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration
Specifically, our C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) within a new coarse-to-fine training scheme.
Routing Recovery for UAV Networks with Deliberate Attacks: A Reinforcement Learning based Approach
The unmanned aerial vehicle (UAV) network is popular these years due to its various applications.
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement
Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images.
Unsupervised Decomposition Networks for Bias Field Correction in MR Image
Many retrospective algorithms were developed to facilitate the bias correction, to which the deep learning-based methods outperformed.
BotHawk: An Approach for Bots Detection in Open Source Software Projects
Identifying bot accounts and behavior is a challenging task in the OSS project.
SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
Recent advances in large language models (LLMs) have demonstrated notable progress on many mathematical benchmarks.
Refining the Optimization Target for Automatic Univariate Time Series Anomaly Detection in Monitoring Services
Time series anomaly detection is crucial for industrial monitoring services that handle a large volume of data, aiming to ensure reliability and optimize system performance.
Householder Projector for Unsupervised Latent Semantics Discovery
Generative Adversarial Networks (GANs), especially the recent style-based generators (StyleGANs), have versatile semantics in the structured latent space.
Generalizing Graph ODE for Learning Complex System Dynamics across Environments
In practice, however, we might observe multiple systems that are generated across different environments, which differ in latent exogenous factors such as temperature and gravity.
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
Coordinating Multiple Resources for Optimal Postdisaster Operation of Interdependent Electric Power and Natural Gas Distribution Systems
Specifically, 1) on the supply side, the fuel supply issue of different types of generators for emergency use is considered and the fuel delivery process among different fuel facilities is mathematically formulated; 2) on the demand side, a zonewise method is proposed for integrated dispatch of power and gas demand responses; and 3) in the repair process, a varying efficiency related to the repair units at work is introduced to accurately model repairs.
AOG-LSTM: An adaptive attention neural network for visual storytelling
Moreover, the existing method of alleviating error accumulation based on replacing reference words does not take into account the different effects of each word.
Ranked #20 on
Visual Storytelling
on VIST
Leveraging Brain Modularity Prior for Interpretable Representation Learning of fMRI
Resting-state functional magnetic resonance imaging (rs-fMRI) can reflect spontaneous neural activities in brain and is widely used for brain disorder analysis. Previous studies propose to extract fMRI representations through diverse machine/deep learning methods for subsequent analysis.
Learning by Analogy: Diverse Questions Generation in Math Word Problem
We then feed them to a question generator together with the scenario to obtain the corresponding diverse questions, forming a new MWP with a variety of questions and equations.
Shuffled Autoregression For Motion Interpolation
However, motion interpolation is a more complex problem that takes isolated poses (e. g., only one start pose and one end pose) as input.
Gotta: Generative Few-shot Question Answering by Prompt-based Cloze Data Augmentation
Few-shot question answering (QA) aims at precisely discovering answers to a set of questions from context passages while only a few training samples are available.
Professional Basketball Player Behavior Synthesis via Planning with Diffusion
To accomplish reward-guided trajectory generation, conditional sampling is introduced to condition the diffusion model on the value function and conduct classifier-guided sampling.
Can Directed Graph Neural Networks be Adversarially Robust?
This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs.
CAROM Air -- Vehicle Localization and Traffic Scene Reconstruction from Aerial Videos
Road traffic scene reconstruction from videos has been desirable by road safety regulators, city planners, researchers, and autonomous driving technology developers.
Universality and Limitations of Prompt Tuning
The limitations of prompt tuning for limited-depth transformers are first proved by constructing a set of datasets, that cannot be memorized by a prompt of any length for a given single encoder layer.
Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making
Pre-trained language models (PLMs) have been widely used to underpin various downstream tasks.
TLNets: Transformation Learning Networks for long-range time-series prediction
Note that the FT and SVD blocks are capable of learning global information, while the Conv blocks focus on learning local information.
Exploiting Correlations Between Contexts and Definitions with Multiple Definition Modeling
In this paper, we carefully design a new task called Multiple Definition Modeling (MDM) that pool together all contexts and definition of target words.
STAR: Boosting Low-Resource Event Extraction by Structure-to-Text Data Generation with Large Language Models
Structure prediction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies, thus they still heavily rely on task-specific training data to obtain reasonable performance.
Lion: Adversarial Distillation of Closed-Source Large Language Model
The practice of transferring knowledge from a sophisticated, closed-source large language model (LLM) to a compact, open-source LLM has garnered considerable attention.
Whisper-KDQ: A Lightweight Whisper via Guided Knowledge Distillation and Quantization for Efficient ASR
Due to the rapid development of computing hardware resources and the dramatic growth of data, pre-trained models in speech recognition, such as Whisper, have significantly improved the performance of speech recognition tasks.
Self-Supervised Learning for Point Clouds Data: A Survey
We first present an innovative taxonomy, categorizing the existing SSL methods into four broad categories based on the pretexts' characteristics.
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning
Recent studies show that, without any prior model, the unsupervised restoration learning problem can be optimally formulated as an optimal transport (OT) problem, which has shown promising performance on denoising tasks to approach the performance of supervised methods.
Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy
We propose a binary stochastic learning algorithm that modifies all elementary neural network operations, by introducing (i) stochastic binarization of both the forwarding signals and the activation function derivatives, (ii) signed binarization of the backpropagating errors, and (iii) step-wised weight updates.
Bi-Level Attention Graph Neural Networks
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs).
Dehazing-NeRF: Neural Radiance Fields from Hazy Images
Our method simulates the physical imaging process of hazy images using an atmospheric scattering model, and jointly learns the atmospheric scattering model and a clean NeRF model for both image dehazing and novel view synthesis.
Collaborative Feature Learning for Fine-grained Facial Forgery Detection and Segmentation
An important topic in manipulation detection is the localization of the fake regions.
ChatPLUG: Open-Domain Generative Dialogue System with Internet-Augmented Instruction Tuning for Digital Human
In this paper, we present ChatPLUG, a Chinese open-domain dialogue system for digital human applications that instruction finetunes on a wide range of dialogue tasks in a unified internet-augmented format.
RRHF: Rank Responses to Align Language Models with Human Feedback without tears
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models with human preferences, significantly enhancing the quality of interactions between humans and these models.
Efficient automatic segmentation for multi-level pulmonary arteries: The PARSE challenge
Efficient automatic segmentation of multi-level (i. e. main and branch) pulmonary arteries (PA) in CTPA images plays a significant role in clinical applications.
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time.
HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion
LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision.
Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning
With this dataset, we developed HRV models that combine signal processing and ML to directly infer HRV.
PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models
Moreover, our analysis showed that DT models with 10 to 20 input features usually have good accuracy, while are several magnitude smaller in model sizes and faster in inference time.
W2KPE: Keyphrase Extraction with Word-Word Relation
This paper describes our submission to ICASSP 2023 MUG Challenge Track 4, Keyphrase Extraction, which aims to extract keyphrases most relevant to the conference theme from conference materials.
NLUT: Neural-based 3D Lookup Tables for Video Photorealistic Style Transfer
However, existing methods obtain stylized video sequences by performing frame-by-frame photorealistic style transfer, which is inefficient and does not ensure the temporal consistency of the stylized video.
How well do Large Language Models perform in Arithmetic tasks?
Large language models have emerged abilities including chain-of-thought to answer math word problems step by step.
RiDDLE: Reversible and Diversified De-identification with Latent Encryptor
This work presents RiDDLE, short for Reversible and Diversified De-identification with Latent Encryptor, to protect the identity information of people from being misused.
Toward Polar Sea-Ice Classification using Color-based Segmentation and Auto-labeling of Sentinel-2 Imagery to Train an Efficient Deep Learning Model
Global warming is an urgent issue that is generating catastrophic environmental changes, such as the melting of sea ice and glaciers, particularly in the polar regions.
Weighted Sampling for Masked Language Modeling
Experiments on the Semantic Textual Similarity benchmark (STS) show that WSBERT significantly improves sentence embeddings over BERT.
LSR: A Light-Weight Super-Resolution Method
A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work.
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining
Communication scheduling has been shown to be effective in accelerating distributed training, which enables all-reduce communications to be overlapped with backpropagation computations.
Theoretical Evaluation of the Capacity-Achieving Distribution for IM-DD Fiber-Optic Channels
For the IM-DD system under peak power constraint (PPC), a dynamic-assignment BA algorithm is applied to find the capacity-achieving distribution with minimum cardinality.
Designing a 3D-Aware StyleNeRF Encoder for Face Editing
For more flexible face manipulation, we then design a dual-branch StyleFlow module to transfer the StyleNeRF codes with disentangled geometry and texture flows.
Linear-scaling kernels for protein sequences and small molecules outperform deep learning while providing uncertainty quantitation and improved interpretability
We compare the performance of xGPR with the reported performance of various deep learning models on 20 benchmarks, including small molecule, protein sequence and tabular data.
Ranked #6 on
Formation Energy
on QM9
Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling
To alleviate this limitation, we propose Moleformer, a novel Transformer architecture that takes nodes (atoms) and edges (bonds and nonbonding atom pairs) as inputs and models the interactions among them using rotational and translational invariant geometry-aware spatial encoding.
mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video
In contrast to predominant paradigms of solely relying on sequence-to-sequence generation or encoder-based instance discrimination, mPLUG-2 introduces a multi-module composition network by sharing common universal modules for modality collaboration and disentangling different modality modules to deal with modality entanglement.
Ranked #1 on
Visual Grounding
on RefCOCO+ testA
Foresee What You Will Learn: Data Augmentation for Domain Generalization in Non-stationary Environment
In this paper, we formulate such problems as Evolving Domain Generalization, where a model aims to generalize well on a target domain by discovering and leveraging the evolving pattern of the environment.
From Plate to Prevention: A Dietary Nutrient-aided Platform for Health Promotion in Singapore
In this paper, we share our experience in addressing this issue and attaining medical-grade nutrient intake information to benefit Singaporeans in different aspects.
VQNet 2.0: A New Generation Machine Learning Framework that Unifies Classical and Quantum
Therefore, based on VQNet 1. 0, we further propose VQNet 2. 0, a new generation of unified classical and quantum machine learning framework that supports hybrid optimization.
Mask-then-Fill: A Flexible and Effective Data Augmentation Framework for Event Extraction
We present Mask-then-Fill, a flexible and effective data augmentation framework for event extraction.
Bidirectional Alignment for Domain Adaptive Detection with Transformers
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance.
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation
In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial.
Joint Beamforming Design for Dual-Functional MIMO Radar and Communication Systems Guaranteeing Physical Layer Security
The dual-functional radar and communication (DFRC) technique constitutes a promising next-generation wireless solution, due to its benefits in terms of power consumption, physical hardware, and spectrum exploitation.
Boosting Accuracy and Robustness of Student Models via Adaptive Adversarial Distillation
Thus, recent studies concern about adversarial distillation (AD) that aims to inherit not only prediction accuracy but also adversarial robustness of a robust teacher model under the paradigm of robust optimization.
LEA2: A Lightweight Ensemble Adversarial Attack via Non-overlapping Vulnerable Frequency Regions
However, most existing ensemble attacks depend on numerous substitute models to cover the vulnerable subspace of a target model.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection
This paper attempts to answer this unexplored question by proving a hypothesis: there is a point-labeled dataset where saliency models trained on it can achieve equivalent performance when trained on the densely annotated dataset.
AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs
Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns.
Orthogonal SVD Covariance Conditioning and Latent Disentanglement
Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve covariance conditioning and generalization.
A Generative Approach for Script Event Prediction via Contrastive Fine-tuning
First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.
A Mobility-Aware Deep Learning Model for Long-Term COVID-19 Pandemic Prediction and Policy Impact Analysis
Hence, those models are unable to predict further future.
AGO: Boosting Mobile AI Inference Performance by Removing Constraints on Graph Optimization
Traditional deep learning compilers rely on heuristics for subgraph generation, which impose extra constraints on graph optimization, e. g., each subgraph can only contain at most one complex operator.
Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition
Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis.
Learning from Long-Tailed Noisy Data with Sample Selection and Balanced Loss
Specifically, we separate the noisy training data into clean labeled set and unlabeled set with sample selection, and train the deep neural network in a semi-supervised manner with a balanced loss based on model bias.
Improving Speech Emotion Recognition with Unsupervised Speaking Style Transfer
Once training is completed, EmoAug enriches expressions of emotional speech with different prosodic attributes, such as stress, rhythm and intensity, by feeding different styles into the paralinguistic encoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Introducing Semantics into Speech Encoders
Recent studies find existing self-supervised speech encoders contain primarily acoustic rather than semantic information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+10
Long bet will lose: demystifying seemingly fair gambling via two-armed Futurity bandit
No matter how much some gamblers occasionally win, as long as they continue to gamble, sooner or later they will lose more to the casino, which is the so-called long bet will lose.
Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?
In this paper, we systematically examine the performance of machine learning models for the clinical prediction task based on the EHR, especially physiological time series.
Deep Factorization Model for Robust Recommendation
Recently, malevolent user hacking has become a huge problem for real-world companies.
Title2Event: Benchmarking Open Event Extraction with a Large-scale Chinese Title Dataset
To the best of our knowledge, it is currently the largest manually-annotated Chinese dataset for open event extraction.
SEVGGNet-LSTM: a fused deep learning model for ECG classification
This paper presents a fused deep learning algorithm for ECG classification.
Deep Latent Mixture Model for Recommendation
Recent advances in neural networks have been successfully applied to many tasks in online recommendation applications.
Pronunciation Generation for Foreign Language Words in Intra-Sentential Code-Switching Speech Recognition
In this paper, we make use of limited code-switching data as driving materials and explore a shortcut to quickly develop intra-sentential code-switching recognition skill on the commissioned native language acoustic model, where we propose a data-driven method to make the seed lexicon which is used to train grapheme-to-phoneme model to predict mapping pronunciations for foreign language word in code-switching sentences.
Synthetic Data Supervised Salient Object Detection
In this paper, we propose a novel yet effective method for SOD, coined SODGAN, which can generate infinite high-quality image-mask pairs requiring only a few labeled data, and these synthesized pairs can replace the human-labeled DUTS-TR to train any off-the-shelf SOD model.
OSS Mentor A framework for improving developers contributions via deep reinforcement learning
In open source project governance, there has been a lot of concern about how to measure developers' contributions.
Robust Ellipse Fitting Based on Maximum Correntropy Criterion With Variable Center
For single ellipse fitting, we formulate a non-convex optimization problem to estimate the kernel bandwidth and center and divide it into two subproblems, each estimating one parameter.
ALT: Boosting Deep Learning Performance by Breaking the Wall between Graph and Operator Level Optimizations
Deep learning models rely on highly optimized tensor libraries for efficient inference on heterogeneous hardware.
Survey of Deep Learning for Autonomous Surface Vehicles in the Marine Environment
Within the next several years, there will be a high level of autonomous technology that will be available for widespread use, which will reduce labor costs, increase safety, save energy, enable difficult unmanned tasks in harsh environments, and eliminate human error.
Code Recommendation for Open Source Software Developers
In this paper, we formulate the novel problem of code recommendation, whose purpose is to predict the future contribution behaviors of developers given their interaction history, the semantic features of source code, and the hierarchical file structures of projects.
Exploiting Mixed Unlabeled Data for Detecting Samples of Seen and Unseen Out-of-Distribution Classes
We propose the Adaptive In-Out-aware Learning (AIOL) method, in which we employ the appropriate temperature to adaptively select potential ID and OOD samples from the mixed unlabeled data and consider the entropy over them for OOD detection.
Feature Reconstruction Attacks and Countermeasures of DNN training in Vertical Federated Learning
To address this problem, we develop a novel feature protection scheme against the reconstruction attack that effectively misleads the search to some pre-specified random values.
GENHOP: An Image Generation Method Based on Successive Subspace Learning
In the first module, it builds a sequence of high-to-low dimensional subspaces through a sequence of whitening processes, each of which contains samples of joint-spatial-spectral representation.
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
To this end, we develop an approach that can effectively take advantage of the sparsity of matrix operations in GCN inference to significantly reduce the computational overhead.
Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure based FoG Detection
The proposed scheme also sheds light on improving subject-level clinical studies from other scenarios as it can be integrated with many existing deep architectures.
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs
For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures.
RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection
The task of out-of-distribution (OOD) detection is crucial for deploying machine learning models in real-world settings.
Multimodal Graph Learning for Deepfake Detection
We evaluate the effectiveness of our method through extensive experiments on widely-used benchmarks and demonstrate that our method outperforms the state-of-the-art detectors in terms of generalization ability and robustness against unknown disturbances.
Training and Tuning Generative Neural Radiance Fields for Attribute-Conditional 3D-Aware Face Generation
To this end, we introduce a conditional GNeRF model that uses specific attribute labels as input in order to improve the controllabilities and disentangling abilities of 3D-aware generative models.
A flexible empirical Bayes approach to multiple linear regression and connections with penalized regression
We introduce a new empirical Bayes approach for large-scale multiple linear regression.
Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression
We showed that this linear model could predict gene expression levels using promoter sequences with a performance comparable to deep neural network models.
DICE: Data-Efficient Clinical Event Extraction with Generative Models
Event extraction for the clinical domain is an under-explored research area.
UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene
Finally, we jointly optimize a hyper network to realize the scene photorealistic style transfer of arbitrary style images.
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation
In this paper, we propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components.
Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search
In this paper, we propose NSUBS with two innovations to tackle the challenges: (1) A novel encoder-decoder neural network architecture to dynamically compute the matching information between the query and the target graphs at each search state; (2) A novel look-ahead loss function for training the policy network.
STT: Soft Template Tuning for Few-Shot Adaptation
Significantly, it can even outperform the time- and resource-consuming fine-tuning method on sentiment classification tasks.
Cross-Domain Cross-Set Few-Shot Learning via Learning Compact and Aligned Representations
Different from previous cross-domain FSL work (CD-FSL) that considers the domain shift between base and novel classes, the new problem, termed cross-domain cross-set FSL (CDSC-FSL), requires few-shot learners not only to adapt to the new domain, but also to be consistent between different domains within each novel class.
Batch-efficient EigenDecomposition for Small and Medium Matrices
EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications.
PI-Trans: Parallel-ConvMLP and Implicit-Transformation Based GAN for Cross-View Image Translation
For semantic-guided cross-view image translation, it is crucial to learn where to sample pixels from the source view image and where to reallocate them guided by the target view semantic map, especially when there is little overlap or drastic view difference between the source and target images.
Graph-based Molecular Representation Learning
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem.
Improving Covariance Conditioning of the SVD Meta-layer by Orthogonality
Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities.
Unsupervised High-Resolution Portrait Gaze Correction and Animation
Solving this problem using an unsupervised method remains an open problem, especially for high-resolution face images in the wild, which are not easy to annotate with gaze and head pose labels.
Scalable K-FAC Training for Deep Neural Networks with Distributed Preconditioning
The second-order optimization methods, notably the D-KFAC (Distributed Kronecker Factored Approximate Curvature) algorithms, have gained traction on accelerating deep neural network (DNN) training on GPU clusters.
DFGC 2022: The Second DeepFake Game Competition
There is a two-party game between DeepFake creators and defenders.
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results.
Pisces: Efficient Federated Learning via Guided Asynchronous Training
Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration.
Region-enhanced Deep Graph Convolutional Networks for Rumor Detection
Specifically, a novel region-enhanced deep graph convolutional network (RDGCN) that enhances the propagation features of rumors by learning regionalized propagation patterns and trains to learn the propagation patterns by unsupervised learning is proposed.
ULTRA: Uncertainty-aware Label Distribution Learning for Breast Tumor Cellularity Assessment
Therefore, automatic TC estimation is significant in clinical practice.
Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives
Following SimCSE, contrastive learning based methods have achieved the state-of-the-art (SOTA) performance in learning sentence embeddings.
Abnormal Signal Recognition with Time-Frequency Spectrogram: A Deep Learning Approach
With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system.
Exposing Fine-Grained Adversarial Vulnerability of Face Anti-Spoofing Models
These fine-grained adversarial examples can be used for selecting robust backbone networks and auxiliary features.
On the Eigenvalues of Global Covariance Pooling for Fine-grained Visual Recognition
Inspired by this observation, we propose a network branch dedicated to magnifying the importance of small eigenvalues.
Ranked #4 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Visual Categorization
+1
Masked Jigsaw Puzzle: A Versatile Position Embedding for Vision Transformers
In particular, MJP first shuffles the selected patches via our block-wise random jigsaw puzzle shuffle algorithm, and their corresponding PEs are occluded.
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks.
Ranked #1 on
Image Captioning
on COCO Captions
Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy
We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method.
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method
The experimental results show that the image denoising method of ZrO2 ceramic bearing ball surface defect based on cartoon-texture decomposition model can denoise while retaining the image details.
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
Intelligent Reflecting Surface Configurations for Smart Radio Using Deep Reinforcement Learning
Intelligent reflecting surface (IRS) is envisioned to change the paradigm of wireless communications from "adapting to wireless channels" to "changing wireless channels".
COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas
In this work, we propose COSPLAY(COncept Set guided PersonaLized dialogue generation Across both partY personas) that considers both parties as a "team": expressing self-persona while keeping curiosity toward the partner, leading responses around mutual personas, and finding the common ground.
On the Equity of Nuclear Norm Maximization in Unsupervised Domain Adaptation
Nuclear norm maximization has shown the power to enhance the transferability of unsupervised domain adaptation model (UDA) in an empirical scheme.
Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences
A DPP-based NSP representation and actionable NSP discovery method EINSP introduces novel and significant contributions for NSA and sequence analysis: (1) it represents NSPs by a determinantal point process (DPP) based graph; (2) it quantifies actionable NSPs in terms of their statistical significance, diversity, and strength of explicit/implicit element/pattern relations; and (3) it models and measures both explicit and implicit element/pattern relations in the DPP-based NSP graph to represent direct and indirect couplings between NSP items, elements and patterns.
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation
Existing compression algorithms are not fit for semantic segmentation, as the lack of obvious and concentrated regions of interest (RoIs) forces the adoption of uniform compression strategies, leading to low compression ratios or accuracy.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.
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Knowledge Graph Completion
on DPB-5L (French)
Physical Layer Security Assisted Computation Offloading in Intelligently Connected Vehicle Networks
In this paper, we propose a secure computation offloading scheme (SCOS) in intelligently connected vehicle (ICV) networks, aiming to minimize overall latency of computing via offloading part of computational tasks to nearby servers in small cell base stations (SBSs), while securing the information delivered during offloading and feedback phases via physical layer security.
On Supervised Feature Selection from High Dimensional Feature Spaces
The application of machine learning to image and video data often yields a high dimensional feature space.
Efficient Training of the Memristive Deep Belief Net Immune to Non-Idealities of the Synaptic Devices
Nevertheless, the implementation of the VMM needs complex peripheral circuits and the complexity further increases since non-idealities of memristive devices prevent precise conductance tuning (especially for the online training) and largely degrade the performance of the deep neural networks (DNNs).
Improving Event Representation via Simultaneous Weakly Supervised Contrastive Learning and Clustering
Representations of events described in text are important for various tasks.