Search Results for author:
Found 138 papers, 62 papers with code
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.
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.
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.
QCRI Live Speech Translation System
This paper presents QCRI{'}s Arabic-to-English live speech translation system.
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.
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.
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.
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 #5 on
Skeleton Based Action Recognition
on UAV-Human
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.
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.
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.
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 #55 on
Skeleton Based Action Recognition
on NTU RGB+D
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.
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Deep learning based medical image diagnosis has shown great potential in clinical medicine.
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.
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 #27 on
Skeleton Based Action Recognition
on NTU RGB+D
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
Multi-Aspect Sentiment Analysis with Latent Sentiment-Aspect Attribution
In this paper, we introduce a new framework called the sentiment-aspect attribution module (SAAM).
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.
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
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.
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.
Improving Authorship Verification using Linguistic Divergence
It is also one of the first to use Deep Language Models in this setting.
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.
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.
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.
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.
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.
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.
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 #11 on
Domain Adaptation
on VisDA2017
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.
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 #7 on
Long-tail Learning
on iNaturalist 2018
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Semantic Segmentation by Early Region Proxy
Typical vision backbones manipulate structured features.
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.
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.
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 #1 on
3D Object Detection
on KITTI Cars Easy
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 #38 on
Self-Supervised Image Classification
on ImageNet
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.
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.
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.
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.
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.
Pre-Training Representations of Binary Code Using Contrastive Learning
To the best of our knowledge, COMBO is the first language representation model that incorporates source code, binary code, and comments into contrastive code representation learning and unifies multiple tasks for binary code analysis.
Leveraging Artificial Intelligence on Binary Code Comprehension
We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code.
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.
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.
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.
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.
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.
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 #4 on
Domain Adaptation
on Office-Home
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.
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.
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.
SEAL: Simultaneous Label Hierarchy Exploration And Learning
Label hierarchy is an important source of external knowledge that can enhance classification performance.
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.
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.
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.
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
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.
SplatFlow: Learning Multi-frame Optical Flow via Splatting
The occlusion problem remains a crucial challenge in optical flow estimation (OFE).
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.
Spatial-Temporal Enhanced Transformer Towards Multi-Frame 3D Object Detection
Specifically, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network, which represents queries as nodes in a graph and enables effective modeling of object interactions within a social context.
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.
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.
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 #3 on
Math Word Problem Solving
on MATH
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.
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records.
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.
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.
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.
Information Flow in Self-Supervised Learning
In this paper, we provide a comprehensive toolbox for understanding and enhancing self-supervised learning (SSL) methods through the lens of matrix information theory.
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.
A Critical Look at Classic Test-Time Adaptation Methods in Semantic Segmentation
This pronounced emphasis on classification might lead numerous newcomers and engineers to mistakenly assume that classic TTA methods designed for classification can be directly applied to segmentation.
Implicit Concept Removal of Diffusion Models
To address this, we utilize the intrinsic geometric characteristics of implicit concepts and present the Geom-Erasing, a novel concept removal method based on geometric-driven control.
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.
Covering Number of Real Algebraic Varieties and Beyond: Improved Bounds and Applications
We prove an upper bound on the covering number of real algebraic varieties, images of polynomial maps and semialgebraic sets.
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%.
Deep Tensor Network
In this paper, we delve into the foundational principles of tensor categories, harnessing the universal property of the tensor product to pioneer novel methodologies in deep network architectures.
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.
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.
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.
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.
Dozerformer: Sequence Adaptive Sparse Transformer for Multivariate Time Series Forecasting
(3) Vary, allows queries to selectively attend to keys from a subset of the historical sequence.
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.
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
Augmenting Math Word Problems via Iterative Question Composing
The MMIQC dataset is available on the HuggingFace hub at https://huggingface. co/datasets/Vivacem/MMIQC.
Ranked #38 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.
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.
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.
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.
The Fragile Nature of Road Transportation Systems
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.
Position Paper: What Can Large Language Models Tell Us about Time Series Analysis
Time series analysis is essential for comprehending the complexities inherent in various real-world systems and applications.
Autonomous Data Selection with Language Models for Mathematical Texts
Our method showcases a 2 times increase in pretraining token efficiency compared to state-of-the-art baselines, underscoring the potential of our approach in enhancing models' mathematical reasoning capabilities.
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.
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.
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.
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.
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.
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
For the state-of-the-art (SOTA) model, the MSE is reduced by $33. 3\%$.
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.
Low-cost adaptive obstacle avoidance trajectory control for express delivery drone
This paper studies quadcopters obstacle avoidance trajectory control (OATC) problem for express delivery.
MEEL: Multi-Modal Event Evolution Learning
Finally, we observe that models trained in this way are still struggling to fully comprehend event evolution.
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.
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.
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.
