Search Results for author:
Found 193 papers, 86 papers with code
COVID-19 in Bulgarian Social Media: Factuality, Harmfulness, Propaganda, and Framing
With the emergence of the COVID-19 pandemic, the political and the medical aspects of disinformation merged as the problem got elevated to a whole new level to become the first global infodemic.
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
Nevertheless, how to efficiently model the spatial-temporal skeleton graph without introducing extra computation burden is a challenging problem for industrial deployment.
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale.
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Currently, existing surveys on LLM safety primarily focus on specific stages of the LLM lifecycle, e. g., deployment phase or fine-tuning phase, lacking a comprehensive understanding of the entire "lifechain" of LLMs.
COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inference
Transformer-based models have demonstrated superior performance in various fields, including natural language processing and computer vision.
TeLLMe: An Energy-Efficient Ternary LLM Accelerator for Prefilling and Decoding on Edge FPGAs
Deploying large language models (LLMs) on edge platforms is challenged by their high computational and memory demands.
VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform
Offline experiments and large-scale online A/B test demonstrates the superiority of KuaiMod: KuaiMod achieves the best moderation performance on our benchmark.
InstructEngine: Instruction-driven Text-to-Image Alignment
Reinforcement Learning from Human/AI Feedback (RLHF/RLAIF) has been extensively utilized for preference alignment of text-to-image models.
Integrating Artificial Intelligence with Human Expertise: An In-depth Analysis of ChatGPT's Capabilities in Generating Metamorphic Relations
Method: The initial phase evaluates MRs generated by GPT-3. 5 and GPT-4 using criteria from a prior study, followed by an application of an enhanced evaluation framework on MRs created by GPT-4 for a diverse range of nine SUTs, varying from simple programs to complex systems incorporating AI/ML components.
Network-wide Freeway Traffic Estimation Using Sparse Sensor Data: A Dirichlet Graph Auto-Encoder Approach
We propose DGAE, a novel inductive graph representation model that addresses these challenges through theoretically derived DEFP for Directed graph (DEFP4D), enhanced spatial representation learning via DEFP4D-guided latent space encoding, and physics-guided propagation mechanisms that separately handles congested and free-flow patterns.
Enhancing Code LLM Training with Programmer Attention
Human attention provides valuable yet underexploited signals for code LLM training, offering a perspective beyond purely machine-driven attention.
DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation
To address this, controllable Text-to-Image (T2I) diffusion offers a potential solution for training data enhancement, which is required to generate diverse OOD scenarios with precise 3D object geometry.
Finite State Automata Inside Transformers with Chain-of-Thought: A Mechanistic Study on State Tracking
Chain-of-Thought (CoT) significantly enhances the performance of large language models (LLMs) across a wide range of tasks, and prior research shows that CoT can theoretically increase expressiveness.
Learning to Generalize without Bias for Open-Vocabulary Action Recognition
Leveraging the effective visual-text alignment and static generalizability from CLIP, recent video learners adopt CLIP initialization with further regularization or recombination for generalization in open-vocabulary action recognition in-context.
CONSTRUCTA: Automating Commercial Construction Schedules in Fabrication Facilities with Large Language Models
Automating planning with LLMs presents transformative opportunities for traditional industries, yet remains underexplored.
DAMO: Data- and Model-aware Alignment of Multi-modal LLMs
Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences.
Optimizing Code Runtime Performance through Context-Aware Retrieval-Augmented Generation
Optimizing software performance through automated code refinement offers a promising avenue for enhancing execution speed and efficiency.
MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks
Given a partially observed road network, how can we predict the traffic state of unobserved locations?
Tensor Product Attention Is All You Need
Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory overhead during inference.
On the Interaction in Transient Stability of Two-Inverter Power Systems containing GFL inverter Using Manifold Method
This paper aims to elucidate the interaction mechanisms and define the stability boundaries of systems of two inverters, including GFL, grid-forming (GFM), or grid-supporting (GSP) inverters.
AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition
Based on the adaptive downsampling hierarchical attention framework, AHMSA-Net captures the subtle changes of micro-expressions from different granularities (fine and coarse) by dynamically adjusting the size of the optical flow feature map at each layer.
Micro Expression Recognition
Micro-Expression Recognition
+1
Human-Guided Image Generation for Expanding Small-Scale Training Image Datasets
We develop a multi-modal projection method with theoretical guarantees to facilitate the exploration of both the original and generated images.
Efficient MedSAMs: Segment Anything in Medical Images on Laptop
Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice.
Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
The rapidly developing field of large multimodal models (LMMs) has led to the emergence of diverse models with remarkable capabilities.
Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?
Cross-modal contrastive distillation has recently been explored for learning effective 3D representations.
The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap
Finally, we show that unifying these two computation paradigms -- integrating the flexibility and intelligence of LLMs with the rigorous reasoning abilities of FMs -- has transformative potential for the development of trustworthy AI software systems.
Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference
The ever-increasing sizes of large language models necessitate distributed solutions for fast inference that exploit multi-dimensional parallelism, where computational loads are split across various accelerators such as GPU clusters.
MEMO: Memory-Guided Diffusion for Expressive Talking Video Generation
Recent advances in video diffusion models have unlocked new potential for realistic audio-driven talking video generation.
Scaling Image Tokenizers with Grouped Spherical Quantization
Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a lack of comprehensive analysis of the scaling behaviours.
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models
This work introduces RARE (Retrieval-Augmented Reasoning Enhancement), a versatile extension to the mutual reasoning framework (rStar), aimed at enhancing reasoning accuracy and factual integrity across large language models (LLMs) for complex, knowledge-intensive tasks such as commonsense and medical reasoning.
Locally robust semiparametric estimation of sample selection models without exclusion restrictions
According to the identification result, we propose to estimate the model by a partially linear regression with a nonparametrically generated regressor.
Training and Evaluating Language Models with Template-based Data Generation
To address this limitation, we introduce Template-based Data Generation (TDG), a novel approach that leverages LLMs (GPT-4) to automatically generate parameterized meta-templates, which are then used to synthesize a vast array of high-quality problems and solutions.
Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks
Multispectral object detection, utilizing RGB and TIR (thermal infrared) modalities, is widely recognized as a challenging task.
MEGA: Memory-Efficient 4D Gaussian Splatting for Dynamic Scenes
Furthermore, we introduce an entropy-constrained Gaussian deformation technique that uses a deformation field to expand the action range of each Gaussian and integrates an opacity-based entropy loss to limit the number of Gaussians, thus forcing our model to use as few Gaussians as possible to fit a dynamic scene well.
Prediction by Machine Learning Analysis of Genomic Data Phenotypic Frost Tolerance in Perccottus glenii
Analysis of the genome sequence of Perccottus glenii, the only fish known to possess freeze tolerance, holds significant importance for understanding how organisms adapt to extreme environments, Traditional biological analysis methods are time-consuming and have limited accuracy, To address these issues, we will employ machine learning techniques to analyze the gene sequences of Perccottus glenii, with Neodontobutis hainanens as a comparative group, Firstly, we have proposed five gene sequence vectorization methods and a method for handling ultra-long gene sequences, We conducted a comparative study on the three vectorization methods: ordinal encoding, One-Hot encoding, and K-mer encoding, to identify the optimal encoding method, Secondly, we constructed four classification models: Random Forest, LightGBM, XGBoost, and Decision Tree, The dataset used by these classification models was extracted from the National Center for Biotechnology Information database, and we vectorized the sequence matrices using the optimal encoding method, K-mer, The Random Forest model, which is the optimal model, achieved a classification accuracy of up to 99, 98 , Lastly, we utilized SHAP values to conduct an interpretable analysis of the optimal classification model, Through ten-fold cross-validation and the AUC metric, we identified the top 10 features that contribute the most to the model's classification accuracy, This demonstrates that machine learning methods can effectively replace traditional manual analysis in identifying genes associated with the freeze tolerance phenotype in Perccottus glenii.
Poison-splat: Computation Cost Attack on 3D Gaussian Splatting
However, in this work, we reveal a significant security vulnerability that has been largely overlooked in 3DGS: the computation cost of training 3DGS could be maliciously tampered by poisoning the input data.
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models
The performance of Large Language Models (LLMs) is substantially influenced by the pretraining corpus, which consists of vast quantities of unsupervised data processed by the models.
NaVIP: An Image-Centric Indoor Navigation Solution for Visually Impaired People
We benchmark on two main aspects: 1) positioning system and 2) exploration support, prioritizing training scalability and real-time inference, to validate the prospect of image-based solution towards indoor navigation.
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks
In general, GFlowNets are trained by fitting the forward flow to the backward flow on sampled training objects.
General Preference Modeling with Preference Representations for Aligning Language Models
In this paper, we introduce preference representation learning, an approach that embeds responses into a latent space to capture intricate preference structures efficiently, achieving linear query complexity.
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning
In this paper, we present MalMixer, a malware family classifier using semi-supervised learning that achieves high accuracy with sparse training data.
On the Diagram of Thought
Current large language models (LLMs) demonstrate impressive capabilities but struggle with complex, multi-step reasoning tasks.
One-Shot Diffusion Mimicker for Handwritten Text Generation
Extensive experiments demonstrate that our method can successfully generate handwriting scripts with just one sample reference in multiple languages, even outperforming previous methods using over ten samples.
MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs
Neural Architecture Search (NAS) has proven effective in discovering new Convolutional Neural Network (CNN) architectures, particularly for scenarios with well-defined accuracy optimization goals.
VITA: Towards Open-Source Interactive Omni Multimodal LLM
The remarkable multimodal capabilities and interactive experience of GPT-4o underscore their necessity in practical applications, yet open-source models rarely excel in both areas.
Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions
In the era of the Internet of Everything, operating systems (OSs) face unprecedented challenges posed by an evolving application landscape and increasingly heterogeneous hardware ecosystems.
STS MICCAI 2023 Challenge: Grand challenge on 2D and 3D semi-supervised tooth segmentation
The Semi-supervised Teeth Segmentation (STS) Challenge, a pioneering event in tooth segmentation, was held as a part of the MICCAI 2023 ToothFairy Workshop on the Alibaba Tianchi platform.
Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework
In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information.
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?
More notably, the primary challenge of GPT-4o has significantly transitioned from IK to IG, establishing it as the first LMM advancing towards the knowledge generalization stage.
Network Threshold Games
We use a measure of network cohesiveness -- the k-core -- to provide a novel characterisation of the equilibria.
Fully Test-Time Adaptation for Monocular 3D Object Detection
However, applying this paradigm in Mono 3Det poses significant challenges due to OOD test data causing a remarkable decline in object detection scores.
Fine-grained Image-to-LiDAR Contrastive Distillation with Visual Foundation Models
Contrastive image-to-LiDAR knowledge transfer, commonly used for learning 3D representations with synchronized images and point clouds, often faces a self-conflict dilemma.
Persuasion in Networks: Can the Sender Do Better than Using Public Signals?
Political and advertising campaigns increasingly exploit social networks to spread information and persuade people.
Three-Dimension Collision-Free Trajectory Planning of UAVs Based on ADS-B Information in Low-Altitude Urban Airspace
Consequently, we leverage ADS-B for surveillance and information broadcasting, and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.
Ta-Adapter: Enhancing few-shot CLIP with task-aware encoders
In this work, we introduce Ta-Adapter, a method that equips both the visual and textual encoders of CLIP with task-specific prompts.
A Comprehensive Evaluation on Event Reasoning of Large Language Models
It requires event schema knowledge to perform global reasoning and needs to deal with the diversity of the inter-event relations and the reasoning paradigms.
PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy
This algorithm allows the model to retain gradient information more effectively while ensuring privacy protection, thereby enhancing the model's utility.
A Progressive Framework of Vision-language Knowledge Distillation and Alignment for Multilingual Scene
Pre-trained vision-language (V-L) models such as CLIP have shown excellent performance in many downstream cross-modal tasks.
MEEL: Multi-Modal Event Evolution Learning
Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution.
Low-cost adaptive obstacle avoidance trajectory control for express delivery drone
This paper studies quadcopters obstacle avoidance trajectory control (OATC) problem for express delivery.
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis
With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field.
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
Dynamic Resource Allocation for Virtual Machine Migration Optimization using Machine Learning
Overall, the paragraph effectively communicates the importance of machine learning technology in addressing resource allocation and virtual machine migration challenges in cloud computing.
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
To tackle this, we further propose EATA with Calibration (EATA-C) to separately exploit the reducible model uncertainty and the inherent data uncertainty for calibrated TTA.
Application of Machine Learning Optimization in Cloud Computing Resource Scheduling and Management
Each user requests the cloud computing center to use a certain number of cloud resources at a specific time.
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
Furthermore, alignment between model and human foci in this setting does not seem to dictate the quality of the LLM-generated summaries.
EyeTrans: Merging Human and Machine Attention for Neural Code Summarization
Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets.
Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts
We release our curated AutoMathText dataset to facilitate future research in automated domain-specific data curation.
Position: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various realworld systems and applications.
The Fragile Nature of Road Transportation Systems
Major cities worldwide experience problems with the performance of their road transportation systems, and the continuous increase in traffic demand presents a substantial challenge to the optimal operation of urban road networks and the efficiency of traffic control strategies.
A Time-varying Shockwave Speed Model for Trajectory Reconstruction using Lagrangian and Eulerian Observations
The calibrated shockwave speed of the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory.
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
To this end, various methods have been proposed to create an adaptive filter by incorporating an extra filter (e. g., a high-pass filter) extracted from the graph topology.
Self-supervised Learning of LiDAR 3D Point Clouds via 2D-3D Neural Calibration
First, we propose the learnable transformation alignment to bridge the domain gap between image and point cloud data, converting features into a unified representation space for effective comparison and matching.
Augmenting Math Word Problems via Iterative Question Composing
Despite the advancements in large language models (LLMs) for mathematical reasoning, solving competition-level math problems remains a significant challenge, especially for open-source LLMs without external tools.
Ranked #66 on
Math Word Problem Solving
on MATH
(using extra training data)
MicroNAS: Zero-Shot Neural Architecture Search for MCUs
Neural Architecture Search (NAS) effectively discovers new Convolutional Neural Network (CNN) architectures, particularly for accuracy optimization.
Segment Any Event Streams via Weighted Adaptation of Pivotal Tokens
One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery.
Segment Any Events via Weighted Adaptation of Pivotal Tokens
One pivotal issue at the heart of this endeavor is the precise alignment and calibration of embeddings derived from event-centric data such that they harmoniously coincide with those originating from RGB imagery.
Ranked #1 on
Event-based Object Segmentation
on DSEC-SEG
More than Vanilla Fusion: a Simple, Decoupling-free, Attention Module for Multimodal Fusion Based on Signal Theory
The vanilla fusion methods still dominate a large percentage of mainstream audio-visual tasks.
Sparse Transformer with Local and Seasonal Adaptation for Multivariate Time Series Forecasting
To address this, we propose a Dozer Attention mechanism consisting of three sparse components: (1) Local, each query exclusively attends to keys within a localized window of neighboring time steps.
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation
The learned adaptors empower these diffusion models to generate high-quality images in just a single step.
Model-free Test Time Adaptation for Out-Of-Distribution Detection
We demonstrate the effectiveness of \abbr through comprehensive experiments on multiple OOD detection benchmarks, extensive empirical studies show that \abbr significantly improves the performance of OOD detection over state-of-the-art methods.
CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning
In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks.
Meta Prompting for AI Systems
In this work, we present a comprehensive study of Meta Prompting (MP), an innovative technique reshaping the utilization of language models (LMs) and AI systems in problem-solving and data interaction.
BClean: A Bayesian Data Cleaning System
By evaluating on both real-world and synthetic datasets, we demonstrate that BClean is capable of achieving an F-measure of up to 0. 9 in data cleaning, outperforming existing Bayesian methods by 2% and other data cleaning methods by 15%.
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications
Covering numbers are a powerful tool used in the development of approximation algorithms, randomized dimension reduction methods, smoothed complexity analysis, and others.
Robustness May be More Brittle than We Think under Different Degrees of Distribution Shifts
This indicates that while pre-trained representations may help improve downstream in-distribution performance, they could have minimal or even adverse effects on generalization in certain OOD scenarios of the downstream task if not used properly.
Implicit Concept Removal of Diffusion Models
Subsequently, the model is optimized to identify and disentangle this information, which is then adopted as negative prompts during generation.
From Question to Exploration: Test-Time Adaptation in Semantic Segmentation?
Second, although the teacher-student scheme does enhance the training stability for segmentation TTA in the presence of noisy pseudo-labels and temporal correlation, it cannot directly result in performance improvement compared to the original model without TTA under complex data distribution.
Can pre-trained models assist in dataset distillation?
Dataset Distillation (DD) is a prominent technique that encapsulates knowledge from a large-scale original dataset into a small synthetic dataset for efficient training.
Information Flow in Self-Supervised Learning
In this paper, we conduct a comprehensive analysis of two dual-branch (Siamese architecture) self-supervised learning approaches, namely Barlow Twins and spectral contrastive learning, through the lens of matrix mutual information.
A Fine-Grained Image Description Generation Method Based on Joint Objectives
The goal of fine-grained image description generation techniques is to learn detailed information from images and simulate human-like descriptions that provide coherent and comprehensive textual details about the image content.
Illumination Distillation Framework for Nighttime Person Re-Identification and A New Benchmark
The illumination enhancement branch first estimates an enhanced image from the nighttime image using a nonlinear curve mapping method and then extracts the enhanced features.
FPTQ: Fine-grained Post-Training Quantization for Large Language Models
In this study, we propose a novel W4A8 post-training quantization method for the available open-sourced LLMs, which combines the advantages of both two recipes.
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records.
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection
Multi-view 3D detection based on BEV (bird-eye-view) has recently achieved significant improvements.
Dataset Quantization
Extensive experiments demonstrate that DQ is able to generate condensed small datasets for training unseen network architectures with state-of-the-art compression ratios for lossless model training.
Cumulative Reasoning with Large Language Models
We demonstrate CR's superiority through several complex reasoning tasks: it outperforms existing methods in logical inference tasks with up to a 9. 3% improvement, achieving 98. 04% accuracy on the curated FOLIO wiki dataset.
Ranked #14 on
Math Word Problem Solving
on MATH
GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT
Though limited cases are presented in this paper, GeoGPT can be further extended to various tasks by equipping with more GIS tools, and we think the paradigm of "foundational plus professional" implied in GeoGPT provides an effective way to develop next-generation GIS in this era of large foundation models.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System
However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation.
Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection
Finally, it poses a challenge for the network to distinguish between the positive query and other highly similar queries that are not the best match.
CARMA: Context-Aware Runtime Reconfiguration for Energy-Efficient Sensor Fusion
Autonomous systems (AS) are systems that can adapt and change their behavior in response to unanticipated events and include systems such as aerial drones, autonomous vehicles, and ground/aquatic robots.
SplatFlow: Learning Multi-frame Optical Flow via Splatting
The occlusion problem remains a crucial challenge in optical flow estimation (OFE).
TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving
Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner.
Matrix Information Theory for Self-Supervised Learning
Inspired by this framework, we introduce Matrix-SSL, a novel approach that leverages matrix information theory to interpret the maximum entropy encoding loss as matrix uniformity loss.
Ranked #1 on
Contrastive Learning
on imagenet-1k
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment
To address this issue, we study a more practical SF-UDA task, termed imbalance-agnostic SF-UDA, where the class distributions of both the unseen source domain and unlabeled target domain are unknown and could be arbitrarily skewed.
RelationMatch: Matching In-batch Relationships for Semi-supervised Learning
Semi-supervised learning has achieved notable success by leveraging very few labeled data and exploiting the wealth of information derived from unlabeled data.
Model-Contrastive Federated Domain Adaptation
Federated domain adaptation (FDA) aims to collaboratively transfer knowledge from source clients (domains) to the related but different target client, without communicating the local data of any client.
SEAL: Simultaneous Label Hierarchy Exploration And Learning
Label hierarchy is an important source of external knowledge that can enhance classification performance.
Contrastive Learning Is Spectral Clustering On Similarity Graph
Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works.
Disentangling Writer and Character Styles for Handwriting Generation
In light of this, we propose to disentangle the style representations at both writer and character levels from individual handwritings to synthesize realistic stylized online handwritten characters.
Towards Stable Test-Time Adaptation in Dynamic Wild World
In this paper, we investigate the unstable reasons and find that the batch norm layer is a crucial factor hindering TTA stability.
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data.
Ranked #8 on
Domain Adaptation
on Office-Home
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes.
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks
Specifically, we extend three distinct adversarial attacks to the 3D object detection task, benchmarking the robustness of state-of-the-art LiDAR-based 3D object detectors against attacks on the KITTI and Waymo datasets.
Expanding Small-Scale Datasets with Guided Imagination
Specifically, GIF conducts data imagination by optimizing the latent features of the seed data in the semantically meaningful space of the prior model, resulting in the creation of photo-realistic images with new content.
Moment Estimation for Nonparametric Mixture Models Through Implicit Tensor Decomposition
We present an alternating least squares type numerical optimization scheme to estimate conditionally-independent mixture models in $\mathbb{R}^n$, without parameterizing the distributions.
Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving
Specifically, DucTeacher consists of two curriculums, i. e., (1) domain evolving curriculum seeks to learn from the data progressively to handle data distribution discrepancy by estimating the similarity between domains, and (2) distribution matching curriculum seeks to estimate the class distribution for each unlabeled domain to handle class distribution shifts.
Pre-Training Representations of Binary Code Using Contrastive Learning
We further analyze the impact of human-written and synthetic comments on binary code comprehension tasks, revealing a significant performance disparity.
Leveraging Artificial Intelligence on Binary Code Comprehension
We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code.
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information
It is noted that the recurrent neural network (RNN) is available for the UAV trajectory prediction, in which the long short-term memory (LSTM) is specialized in dealing with the time-series data.
Multi-Scale Multi-Target Domain Adaptation for Angle Closure Classification
Based on these domain-invariant features at different scales, the deep model trained on the source domain is able to classify angle closure on multiple target domains even without any annotations in these domains.
Quantized Adaptive Subgradient Algorithms and Their Applications
Therefore, a threshold quantization strategy with a relatively small error is adopted in QCMD adagrad and QRDA adagrad to improve the signal-to-noise ratio and preserve the sparsity of the model.
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume Segmentation
This work provides a new benchmark for the tooth volume segmentation task, and the experiment can serve as the baseline for future AI-based dental imaging research and clinical application development.
Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation
2) Prototype-based alignment and replay: based on the identified label prototypes, we align both domains and enforce the model to retain previous knowledge.
Unsupervised Visual Representation Learning by Synchronous Momentum Grouping
In this paper, we propose a genuine group-level contrastive visual representation learning method whose linear evaluation performance on ImageNet surpasses the vanilla supervised learning.
Ranked #39 on
Self-Supervised Image Classification
on ImageNet
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty.
Ranked #2 on
3D Object Detection
on KITTI Cars Easy
PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient
First, the difference in feature magnitude between the teacher and the student could enforce overly strict constraints on the student.
Efficient Test-Time Model Adaptation without Forgetting
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w. r. t.
Semantic Segmentation by Early Region Proxy
Typical vision backbones manipulate structured features.
Boost Test-Time Performance with Closed-Loop Inference
Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic random sampling or farthest point sampling to progressively downsample input point clouds, despite the fact that not all points are equally important to the task of object detection.
QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media
Fighting the ongoing COVID-19 infodemic has been declared as one of the most important focus areas by the World Health Organization since the onset of the COVID-19 pandemic.
A Generative Car-following Model Conditioned On Driving Styles
Car-following (CF) modeling, an essential component in simulating human CF behaviors, has attracted increasing research interest in the past decades.
Adaptive Channel Encoding for Point Cloud Analysis
Attention mechanism plays a more and more important role in point cloud analysis and channel attention is one of the hotspots.
Adaptive Channel Encoding Transformer for Point Cloud Analysis
Transformer plays an increasingly important role in various computer vision areas and remarkable achievements have also been made in point cloud analysis.
Lightweight Transformer Backbone for Medical Object Detection
Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection.
Detecting and Identifying Optical Signal Attacks on Autonomous Driving Systems
In our study, we use real data sets and the state-of-the-art machine learning model to evaluate our attack detection scheme and the results confirm the effectiveness of our detection method.
Deep Long-Tailed Learning: A Survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing deep models from a large number of images that follow a long-tailed class distribution.
Generalizable Person Re-identification Without Demographics
However, the convex condition of KL DRO may not hold for overparameterized neural networks, such that applying KL DRO often fails to generalize under distribution shifts in real scenarios.
How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.
A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar
While COVID-19 vaccines are finally becoming widely available, a second pandemic that revolves around the circulation of anti-vaxxer fake news may hinder efforts to recover from the first one.
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis
To handle this prob-lem, a feature representation learning method, named Dual-Neighborhood Deep Fusion Network (DNDFN), is proposed to serve as an improved point cloud encoder for the task of non-idealized point cloud classification.
Opinion Prediction with User Fingerprinting
Experimental results show novel insights that were previously unknown such as better predictions for an increase in dynamic history length, the impact of the nature of the article on performance, thereby laying the foundation for further research.
Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition
Existing long-tailed recognition methods, aiming to train class-balanced models from long-tailed data, generally assume the models would be evaluated on the uniform test class distribution.
Ranked #9 on
Long-tail Learning
on iNaturalist 2018
AdaXpert: Adapting Neural Architecture for Growing Data
To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data.
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
(2) prototype adaptation: based on the generated source prototypes and target pseudo labels, we develop a new robust contrastive prototype adaptation strategy to align each pseudo-labeled target data to the corresponding source prototypes.
Ranked #15 on
Domain Adaptation
on Office-31
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Motivated by the above findings, we propose a novel and simple algorithm called Classifier Calibration with Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated gaussian mixture model.
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
Accurate evaluation of the treatment result on X-ray images is a significant and challenging step in root canal therapy since the incorrect interpretation of the therapy results will hamper timely follow-up which is crucial to the patients' treatment outcome.
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.
AdaSGN: Adapting Joint Number and Model Size for Efficient Skeleton-Based Action Recognition
Existing methods for skeleton-based action recognition mainly focus on improving the recognition accuracy, whereas the efficiency of the model is rarely considered.
Efficient Spatialtemporal Context Modeling for Action Recognition
Inspired from 2D criss-cross attention used in segmentation task, we propose a recurrent 3D criss-cross attention (RCCA-3D) module to model the dense long-range spatiotemporal contextual information in video for action recognition.
Learning Defense Transformers for Counterattacking Adversarial Examples
Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs.
Improving Authorship Verification using Linguistic Divergence
It is also one of the first to use Deep Language Models in this setting.
High-Resolution Segmentation of Tooth Root Fuzzy Edge Based on Polynomial Curve Fitting with Landmark Detection
As the most economical and routine auxiliary examination in the diagnosis of root canal treatment, oral X-ray has been widely used by stomatologists.
StablePose: Learning 6D Object Poses from Geometrically Stable Patches
According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose.
Random matrix description of dynamically backscattered coherent waves propagating in a wide-field-illuminated random medium
We find that the dynamic speckle patterns can be utilized to decouple the singly and multiply backscattered components.
Optics Applied Physics
Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning
In this paper, we investigate whether applying contrastive learning to fine-tuning would bring further benefits, and analytically find that optimizing the contrastive loss benefits both discriminative representation learning and model optimization during fine-tuning.
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM).
The Twelvefold Way of Non-Sequential Lossless Compression
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations, and partitions.
Information Theory Information Theory
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action Recognition
Besides, from the data aspect, we introduce a skeletal data decoupling technique to emphasize the specific characteristics of space/time and different motion scales, resulting in a more comprehensive understanding of the human actions. To test the effectiveness of the proposed method, extensive experiments are conducted on four challenging datasets for skeleton-based gesture and action recognition, namely, SHREC, DHG, NTU-60 and NTU-120, where DSTA-Net achieves state-of-the-art performance on all of them.
Ranked #38 on
Skeleton Based Action Recognition
on NTU RGB+D
Improving Chinese Segmentation-free Word Embedding With Unsupervised Association Measure
Recent work on segmentation-free word embedding(sembei) developed a new pipeline of word embedding for unsegmentated language while avoiding segmentation as a preprocessing step.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical medicine.
Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation
In this work, we propose a method that differentiates the satirical news and true news.
Relation-Aware Transformer for Portfolio Policy Learning
One of the key issues is how to represent the non-stationary price series of assets in a portfolio, which is important for portfolio decisions.
Exemplar Loss for Siamese Network in Visual Tracking
In general, Siamese tracking algorithms, supervised by logistic loss and triplet loss, increase the value of inner product between exemplar template and positive sample while reduce the value of inner product with background sample.
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
As deep learning brings excellent performances to object detection algorithms, Tracking by Detection (TBD) has become the mainstream tracking framework.
Energy-Efficient Cyclical Trajectory Design for UAV-Aided Maritime Data Collection in Wind
We aim to minimize the UAV's energy consumption in completing the task by jointly optimizing the communication time scheduling among the buoys and the UAV's flight trajectory subject to wind effect, which is a non-convex problem and difficult to solve optimally.
Prta: A System to Support the Analysis of Propaganda Techniques in the News
However, little attention has been paid to the specific rhetorical and psychological techniques used to convey propaganda messages.
COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19
There are two main challenges: 1) the discrepancy of data distributions between domains; 2) the task difference between the diagnosis of typical pneumonia and COVID-19.
What and Where: Modeling Skeletons from Semantic and Spatial Perspectives for Action Recognition
The two perspectives are orthogonal and complementary to each other; and by fusing them in a unified framework, our method achieves a more comprehensive understanding of the skeleton data.
Disturbance-immune Weight Sharing for Neural Architecture Search
To alleviate the performance disturbance issue, we propose a new disturbance-immune update strategy for model updating.
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
This task, however, has two main difficulties: (i) the non-stationary price series and complex asset correlations make the learning of feature representation very hard; (ii) the practicality principle in financial markets requires controlling both transaction and risk costs.
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature.
Correcting for Selection Bias in Learning-to-rank Systems
Click data collected by modern recommendation systems are an important source of observational data that can be utilized to train learning-to-rank (LTR) systems.
Skeleton-Based Action Recognition with Multi-Stream Adaptive Graph Convolutional Networks
Second, the second-order information of the skeleton data, i. e., the length and orientation of the bones, is rarely investigated, which is naturally more informative and discriminative for the human action recognition.
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision
Existing methods exploit the joint positions to extract the body-part features from the activation map of the convolutional networks to assist human action recognition.
Online Adaptive Asymmetric Active Learning with Limited Budgets
In these problems, there are two key challenges: the query budget is often limited; the ratio between classes is highly imbalanced.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
In this paper, we seek to exploit rich labeled data from relevant domains to help the learning in the target task with unsupervised domain adaptation (UDA).
Multi-marginal Wasserstein GAN
Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation.
Tanbih: Get To Know What You Are Reading
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
Non-Local Graph Convolutional Networks for Skeleton-Based Action Recognition
However, the topology of the graph is set by hand and fixed over all layers, which may be not optimal for the action recognition task and the hierarchical CNN structures.
Ranked #69 on
Skeleton Based Action Recognition
on NTU RGB+D
Interpretable Complex-Valued Neural Networks for Privacy Protection
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features.
Training Binary Weight Networks via Semi-Binary Decomposition
In this paper, we propose a novel semi-binary decomposition method which decomposes a matrix into two binary matrices and a diagonal matrix.
Two-Step Quantization for Low-Bit Neural Networks
In this paper, we propose a simple yet effective Two-Step Quantization (TSQ) framework, by decomposing the network quantization problem into two steps: code learning and transformation function learning based on the learned codes.
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Ranked #7 on
3D Action Recognition
on Assembly101
Adaptive Cost-sensitive Online Classification
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i) weighted sum of sensitivity and specificity; (ii) weighted misclassification cost.
Egocentric Gesture Recognition Using Recurrent 3D Convolutional Neural Networks With Spatiotemporal Transformer Modules
Gesture is a natural interface in interacting with wearable devices such as VR/AR helmet and glasses.
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition
To make it end-to-end and do not rely on any sophisticated body joint detection algorithm, we further propose a two-stream bilinear model which can learn the guidance from the body joints and capture the spatio-temporal features simultaneously.
QCRI Live Speech Translation System
This paper presents QCRI{'}s Arabic-to-English live speech translation system.
Leveraging Multiple Domains for Sentiment Classification
In addition, the learned feature representation can be used as classifier since our model defines the meaning of feature value and arranges high-level features in a prefixed order, so it is not necessary to train another classifier on top of the new features.
The MGB-2 Challenge: Arabic Multi-Dialect Broadcast Media Recognition
For language modelling, we made available over 110M words crawled from Aljazeera Arabic website Aljazeera. net for a 10 year duration 2000-2011.
Constrained Clustering and Its Application to Face Clustering in Videos
As a result, many pairwise constraints between faces can be easily obtained from the temporal and spatial knowledge of the face tracks.
