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Found 45 papers, 24 papers with code
Central leaves in loop groups
This paper analyses the finer structure of Newton strata in loop groups.
Algebraic Geometry
MCNE: An End-to-End Framework for Learning Multiple Conditional Network Representations of Social Network
Recently, the Network Representation Learning (NRL) techniques, which represent graph structure via low-dimension vectors to support social-oriented application, have attracted wide attention.
Flow Guided Short-term Trackers with Cascade Detection for Long-term Tracking
Object tracking has been studied for decades, but most of the existing works are focused on the short-term tracking.
Long-term Joint Scheduling for Urban Traffic
Recently, the traffic congestion in modern cities has become a growing worry for the residents.
Deep Technology Tracing for High-tech Companies
Technological change and innovation are vitally important, especially for high-tech companies.
Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
To this end, we propose a hierarchical trinity search framework to simultaneously discover efficient architectures for all components (i. e. backbone, neck, and head) of object detector in an end-to-end manner.
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task
The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis.
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter
For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance.
Non-invariance of the Brauer-Manin obstruction for surfaces
In this paper, we study the properties of weak approximation with Brauer-Manin obstruction and the Hasse principle with Brauer-Manin obstruction for surfaces with respect to field extensions of number fields.
Number Theory Algebraic Geometry
Flatband-Induced Itinerant Ferromagnetism in RbCo$_2$Se$_2$
$A$Co$_2$Se$_2$ ($A$=K, Rb, Cs) is a homologue of the iron-based superconductor, $A$Fe$_2$Se$_2$.
Superconductivity Materials Science
Adversarial Driving: Attacking End-to-End Autonomous Driving
As research in deep neural networks advances, deep convolutional networks become promising for autonomous driving tasks.
Distilling Object Detectors via Decoupled Features
To this end, we present a novel distillation algorithm via decoupled features (DeFeat) for learning a better student detector.
Conversational Semantic Role Labeling
Semantic role labeling (SRL) aims to extract the arguments for each predicate in an input sentence.
Domain-Adaptive Pretraining Methods for Dialogue Understanding
Language models like BERT and SpanBERT pretrained on open-domain data have obtained impressive gains on various NLP tasks.
Positive-Unlabeled Data Purification in the Wild for Object Detection
In this paper, we present a positive-unlabeled learning based scheme to expand training data by purifying valuable images from massive unlabeled ones, where the original training data are viewed as positive data and the unlabeled images in the wild are unlabeled data.
CMT: Convolutional Neural Networks Meet Vision Transformers
Vision transformers have been successfully applied to image recognition tasks due to their ability to capture long-range dependencies within an image.
Hire-MLP: Vision MLP via Hierarchical Rearrangement
Previous vision MLPs such as MLP-Mixer and ResMLP accept linearly flattened image patches as input, making them inflexible for different input sizes and hard to capture spatial information.
CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling
Conversational semantic role labeling (CSRL) is believed to be a crucial step towards dialogue understanding.
Interpretable Personalized Experimentation
Black-box heterogeneous treatment effect (HTE) models are increasingly being used to create personalized policies that assign individuals to their optimal treatments.
Thompson Sampling with Unrestricted Delays
We investigate properties of Thompson Sampling in the stochastic multi-armed bandit problem with delayed feedback.
Ensemble Method for Estimating Individualized Treatment Effects
In many medical and business applications, researchers are interested in estimating individualized treatment effects using data from a randomized experiment.
Partial Likelihood Thompson Sampling
We consider the problem of deciding how best to target and prioritize existing vaccines that may offer protection against new variants of an infectious disease.
A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings
Contrastive learning has shown great potential in unsupervised sentence embedding tasks, e. g., SimCSE.
Zero-shot Cross-lingual Conversational Semantic Role Labeling
While conversational semantic role labeling (CSRL) has shown its usefulness on Chinese conversational tasks, it is still under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.
Learning Localization-aware Target Confidence for Siamese Visual Tracking
In this paper, to alleviate this misalignment, we propose a novel tracking paradigm, called SiamLA.
Learning Locality and Isotropy in Dialogue Modeling
Existing dialogue modeling methods have achieved promising performance on various dialogue tasks with the aid of Transformer and the large-scale pre-trained language models.
LDRNet: Enabling Real-time Document Localization on Mobile Devices
Aiming at improving the responsiveness of the IDV process, we propose a new document localization model for mobile devices, LDRNet, to Localize the identity Document in Real-time.
Multi-modal Siamese Network for Entity Alignment
To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect.
Ranked #7 on
Multi-modal Entity Alignment
on UMVM-oea-d-w-v1
(using extra training data)
A Man-in-the-Middle Attack against Object Detection Systems
While some attacks are limited by strict assumptions on access to the detection system, we propose a novel hardware attack inspired by Man-in-the-Middle attacks in cryptography.
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize very well to new unseen relations.
Adversarial Detection: Attacking Object Detection in Real Time
Intelligent robots rely on object detection models to perceive the environment.
Distributed Black-box Attack against Image Classification Cloud Services
Black-box adversarial attacks can fool image classifiers into misclassifying images without requiring access to model structure and weights.
FastMIM: Expediting Masked Image Modeling Pre-training for Vision
This paper presents FastMIM, a simple and generic framework for expediting masked image modeling with the following two steps: (i) pre-training vision backbones with low-resolution input images; and (ii) reconstructing Histograms of Oriented Gradients (HOG) feature instead of original RGB values of the input images.
GTV: Generating Tabular Data via Vertical Federated Learning
Conditional vector for tabular GANs is a valuable tool to control specific features of generated data.
Geometry-Aware Attenuation Field Learning for Sparse-View CBCT Reconstruction
This paper proposes a novel attenuation field encoder-decoder framework by first encoding the volumetric feature from multi-view X-ray projections, then decoding it into the desired attenuation field.
VCSUM: A Versatile Chinese Meeting Summarization Dataset
Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data.
Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion.
Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content.
Multi-View Vertebra Localization and Identification from CT Images
Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae.
Fine-grained Conversational Decoding via Isotropic and Proximal Search
General-purpose text decoding approaches are usually adopted for dialogue response generation.
CLIP in Medical Imaging: A Comprehensive Survey
With the aim of facilitating a deeper understanding of this promising direction, this survey offers an in-depth exploration of the CLIP paradigm within the domain of medical imaging, regarding both refined CLIP pre-training and CLIP-driven applications.
Data-efficient Large Vision Models through Sequential Autoregression
Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding.
NiteDR: Nighttime Image De-Raining with Cross-View Sensor Cooperative Learning for Dynamic Driving Scenes
Specifically, we introduce cooperative learning between visible and infrared images captured by different sensors.
SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection
The quality, quantity, and diversity of the infrared dataset are critical to the detection of small targets.
Learning From Correctness Without Prompting Makes LLM Efficient Reasoner
Large language models (LLMs) have demonstrated outstanding performance across various tasks, yet they still exhibit limitations such as hallucination, unfaithful reasoning, and toxic content.
