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Found 79 papers, 41 papers with code
A Language Model based Framework for New Concept Placement in Ontologies
In all steps, we propose to leverage neural methods, where we apply embedding-based methods and contrastive learning with Pre-trained Language Models (PLMs) such as BERT for edge search, and adapt a BERT fine-tuning-based multi-label Edge-Cross-encoder, and Large Language Models (LLMs) such as GPT series, FLAN-T5, and Llama 2, for edge selection.
Understanding Practical Membership Privacy of Deep Learning
We apply a state-of-the-art membership inference attack (MIA) to systematically test the practical privacy vulnerability of fine-tuning large image classification models. We focus on understanding the properties of data sets and samples that make them vulnerable to membership inference.
Language Models as Hierarchy Encoders
Interpreting hierarchical structures latent in language is a key limitation of current language models (LMs).
One-Dimensional Adapter to Rule Them All: Concepts, Diffusion Models and Erasing Applications
The prevalent use of commercial and open-source diffusion models (DMs) for text-to-image generation prompts risk mitigation to prevent undesired behaviors.
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning. paper with code
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
Ranked #6 on
Long-tail Learning
on CIFAR-10-LT (ρ=10)
A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning
However, existing generalization analysis of such losses is still coarse-grained and fragmented, failing to explain some empirical results.
Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals
We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach.
Exploring Large Language Models for Ontology Alignment
This work investigates the applicability of recent generative Large Language Models (LLMs), such as the GPT series and Flan-T5, to ontology alignment for identifying concept equivalence mappings across ontologies.
Revisiting AUC-oriented Adversarial Training with Loss-Agnostic Perturbations
On top of this, we can show that: 1) Under mild conditions, AdAUC can be optimized equivalently with score-based or instance-wise-loss-based perturbations, which is compatible with most of the popular adversarial example generation methods.
DeepOnto: A Python Package for Ontology Engineering with Deep Learning
Integrating deep learning techniques, particularly language models (LMs), with knowledge representation techniques like ontologies has raised widespread attention, urging the need of a platform that supports both paradigms.
Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement
Mentions of new concepts appear regularly in texts and require automated approaches to harvest and place them into Knowledge Bases (KB), e. g., ontologies and taxonomies.
Trend-Based SAC Beam Control Method with Zero-Shot in Superconducting Linear Accelerator
To validate the effectiveness of our method, two different typical beam control tasks were performed on China Accelerator Facility for Superheavy Elements (CAFe II) and a light particle injector(LPI) respectively.
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference
DNNs are widely used but face significant computational costs due to matrix multiplications, especially from data movement between the memory and processing units.
Augment and Criticize: Exploring Informative Samples for Semi-Supervised Monocular 3D Object Detection
In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework.
To Make Yourself Invisible with Adversarial Semantic Contours
In this paper, we propose Adversarial Semantic Contour (ASC), an MAP estimate of a Bayesian formulation of sparse attack with a deceived prior of object contour.
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
To address this issue, we proposed a novel method of Augmenting data with Adversarial examples via a Wavelet module (AdvWavAug), an on-manifold adversarial data augmentation technique that is simple to implement.
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking
In our benchmark, we evaluate the robustness of 55 typical deep learning models on ImageNet with diverse architectures (e. g., CNNs, Transformers) and learning algorithms (e. g., normal supervised training, pre-training, adversarial training) under numerous adversarial attacks and out-of-distribution (OOD) datasets.
Language Model Analysis for Ontology Subsumption Inference
Investigating whether pre-trained language models (LMs) can function as knowledge bases (KBs) has raised wide research interests recently.
Reveal the Unknown: Out-of-Knowledge-Base Mention Discovery with Entity Linking
We propose BLINKout, a new BERT-based Entity Linking (EL) method which can identify mentions that do not have corresponding KB entities by matching them to a special NIL entity.
Open-Vocabulary Object Detection With an Open Corpus
For the classification task, we introduce an open corpus classifier by reconstructing original classifier with similar words in text space.
Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection
Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision.
OpenAUC: Towards AUC-Oriented Open-Set Recognition
In this paper, a systematic analysis reveals that most existing metrics are essentially inconsistent with the aforementioned goal of OSR: (1) For metrics extended from close-set classification, such as Open-set F-score, Youden's index, and Normalized Accuracy, a poor open-set prediction can escape from a low performance score with a superior close-set prediction.
Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective
We conduct comprehensive transferable attacks against multiple DNNs to demonstrate the effectiveness of the proposed method.
The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering.
Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability
Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning.
MaxMatch: Semi-Supervised Learning with Worst-Case Consistency
In recent years, great progress has been made to incorporate unlabeled data to overcome the inefficiently supervised problem via semi-supervised learning (SSL).
Optimizing Partial Area Under the Top-k Curve: Theory and Practice
Finally, the experimental results on four benchmark datasets validate the effectiveness of our proposed framework.
AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems
Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem.
Optimizing Two-way Partial AUC with an End-to-end Framework
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
Machine Learning-Friendly Biomedical Datasets for Equivalence and Subsumption Ontology Matching
Ontology Matching (OM) plays an important role in many domains such as bioinformatics and the Semantic Web, and its research is becoming increasingly popular, especially with the application of machine learning (ML) techniques.
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on
Virtual try-on(VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based(PB) and Parser-Free(PF) by whether relying on the parser information to mask the persons' clothes and synthesize try-on images.
Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
Finally, we propose a weakly supervised object localization-based approach to extract multi-scale local features, to form a multi-view pipeline.
D^2ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention
DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing.
Contextual Semantic Embeddings for Ontology Subsumption Prediction
Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence.
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains
Notably, our methods outperform state-of-the-art approaches by up to 7. 71\% (towards coarse-grained domains) and 25. 91\% (towards fine-grained domains) on average.
Reading-strategy Inspired Visual Representation Learning for Text-to-Video Retrieval
In this work, we concentrate on video representation learning, an essential component for text-to-video retrieval.
Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Survey
Machine learning especially deep neural networks have achieved great success but many of them often rely on a number of labeled samples for supervision.
BERTMap: A BERT-based Ontology Alignment System
Ontology alignment (a. k. a ontology matching (OM)) plays a critical role in knowledge integration.
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Unrestricted Adversarial Attacks on ImageNet Competition
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input.
Adversarial Attacks on ML Defense Models Competition
Due to the vulnerability of deep neural networks (DNNs) to adversarial examples, a large number of defense techniques have been proposed to alleviate this problem in recent years.
D$^2$ETR: Decoder-Only DETR with Computationally Efficient Cross-Scale Attention
DETR is the first fully end-to-end detector that predicts a final set of predictions without post-processing.
Prior-Guided Deep Interference Mitigation for FMCW Radars
The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals.
AdvDrop: Adversarial Attack to DNNs by Dropping Information
Human can easily recognize visual objects with lost information: even losing most details with only contour reserved, e. g. cartoon.
DRDF: Determining the Importance of Different Multimodal Information with Dual-Router Dynamic Framework
In multimodal tasks, we find that the importance of text and image modal information is different for different input cases, and for this motivation, we propose a high-performance and highly general Dual-Router Dynamic Framework (DRDF), consisting of Dual-Router, MWF-Layer, experts and expert fusion unit.
When All We Need is a Piece of the Pie: A Generic Framework for Optimizing Two-way Partial AUC.
The critical challenge along this course lies in the difficulty of performing gradient-based optimization with end-to-end stochastic training, even with a proper choice of surrogate loss.
RAMS-Trans: Recurrent Attention Multi-scale Transformer forFine-grained Image Recognition
We propose the recurrent attention multi-scale transformer (RAMS-Trans), which uses the transformer's self-attention to recursively learn discriminative region attention in a multi-scale manner.
Ranked #7 on
Fine-Grained Image Classification
on Stanford Dogs
Fine-Grained Image Classification
Fine-Grained Image Recognition
Monocular 3D Object Detection: An Extrinsic Parameter Free Approach
Due to the lack of insight in industrial application, existing methods on open datasets neglect the camera pose information, which inevitably results in the detector being susceptible to camera extrinsic parameters.
Ranked #9 on
Monocular 3D Object Detection
on KITTI Cars Moderate
(using extra training data)
Sketch, Ground, and Refine: Top-Down Dense Video Captioning
The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling.
When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking
To leverage high performance under low FPRs, we consider an alternative metric for multipartite ranking evaluating the True Positive Rate (TPR) at a given FPR, denoted as TPR@FPR.
Towards Robust Vision Transformer
By using and combining robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness.
Ranked #24 on
Domain Generalization
on ImageNet-C
Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning
In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute between fashion items.
Seeking the Shape of Sound: An Adaptive Framework for Learning Voice-Face Association
Targeting at (a), we propose a two-level modality alignment loss where both global and local information are considered.
Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink
Though it is well known that the performance of deep neural networks (DNNs) degrades under certain light conditions, there exists no study on the threats of light beams emitted from some physical source as adversarial attacker on DNNs in a real-world scenario.
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval
Comprehensive experiments show that the proposed attack achieves a high attack success rate with few queries against the image retrieval systems under the black-box setting.
Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain
The remarkable success in face forgery techniques has received considerable attention in computer vision due to security concerns.
Hierarchical Similarity Learning for Language-based Product Image Retrieval
In this paper, we focus on the cross-modal similarity measurement, and propose a novel Hierarchical Similarity Learning (HSL) network.
$\rm ^{83}Rb$/$\rm ^{83m}Kr$ production and cross-section measurement with 3.4 MeV and 20 MeV proton beams
$\rm ^{83m}Kr$, with a short lifetime, is an ideal calibration source for liquid xenon or liquid argon detectors.
Nuclear Experiment Instrumentation and Detectors
Multiple Pairwise Ranking Networks for Personalized Video Summarization
In this paper, we investigate video summarization in the supervised setting.
The Open Brands Dataset: Unified brand detection and recognition at scale
To the best of our knowledge, it is the largest dataset for brand detection and recognition with rich annotations.
Composite Adversarial Attacks
Adversarial attack is a technique for deceiving Machine Learning (ML) models, which provides a way to evaluate the adversarial robustness.
English-to-Chinese Transliteration with Phonetic Auxiliary Task
Approaching named entities transliteration as a Neural Machine Translation (NMT) problem is common practice.
Heuristic Domain Adaptation
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem.
Sharp Multiple Instance Learning for DeepFake Video Detection
A sharp MIL (S-MIL) is proposed which builds direct mapping from instance embeddings to bag prediction, rather than from instance embeddings to instance prediction and then to bag prediction in traditional MIL.
GAP++: Learning to generate target-conditioned adversarial examples
Different from previous single-target attack models, our model can conduct target-conditioned attacks by learning the relations of attack target and the semantics in image.
Robust Generative Adversarial Network
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations.
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
This paper strives to learn fine-grained fashion similarity.
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
This strategy leads to severe meta shift issues across multiple tasks, meaning the learned prototypes or class descriptors are not stable as each task only involves their own support set.
Self-supervised Adversarial Training
Recent work has demonstrated that neural networks are vulnerable to adversarial examples.
Learning To Characterize Adversarial Subspaces
To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of \textit{adversarial subspaces} where adversarial examples lie.
AdvKnn: Adversarial Attacks On K-Nearest Neighbor Classifiers With Approximate Gradients
Deep neural networks have been shown to be vulnerable to adversarial examples---maliciously crafted examples that can trigger the target model to misbehave by adding imperceptible perturbations.
Self-Supervised Learning For Few-Shot Image Classification
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
Ranked #4 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
Progressive Relation Learning for Group Activity Recognition
Another relation-gating (RG) agent in continuous action space adjusts the high-level semantic graph to pay more attention to group-relevant relations.
Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices
We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases.
Segmented convolutional gated recurrent neural networks for human activity recognition in ultra-wideband radar
In this work, we propose a neural network architecture, namely segmented convolutional gated recurrent neural network (SCGRNN), to recognize human activities based on micro-Doppler spectrograms measured by the ultra-wideband radar.
Bilinear Representation for Language-based Image Editing Using Conditional Generative Adversarial Networks
The task of Language-Based Image Editing (LBIE) aims at generating a target image by editing the source image based on the given language description.
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Ranked #3 on
Recommendation Systems
on Epinions
(using extra training data)
Dual Encoding for Zero-Example Video Retrieval
This paper attacks the challenging problem of zero-example video retrieval.
A Neural Network Aided Approach for LDPC Coded DCO-OFDM with Clipping Distortion
In this paper, a neural network-aided bit-interleaved coded modulation (NN-BICM) receiver is designed to mitigate the nonlinear clipping distortion in the LDPC coded direct currentbiased optical orthogonal frequency division multiplexing (DCOOFDM) systems.
