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Found 83 papers, 40 papers with code
ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model
Large Language Models (LLMs) have advanced Verilog code generation significantly, yet face challenges in data quality, reasoning capabilities, and computational efficiency.
SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes without labeled training examples by leveraging class-level semantic descriptors such as attributes.
ProAI: Proactive Multi-Agent Conversational AI with Structured Knowledge Base for Psychiatric Diagnosis
Most LLM-driven conversational AI systems operate reactively, responding to user prompts without guiding the interaction.
LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning
To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain parameter-efficient fine-tuning method based on inverse Discrete Cosine Transform (iDCT) with selective locations of learnable components.
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction
Sample selection bias (SSB) and data sparsity (DS) are two persistent challenges for post-click conversion rate (CVR) estimation.
Entropy-Based Decoding for Retrieval-Augmented Large Language Models
Augmenting Large Language Models (LLMs) with retrieved external knowledge has proven effective for improving the factual accuracy of generated responses.
Generative Artificial Intelligence-Guided User Studies: An Application for Air Taxi Services
Our study confirms the capability of GenAI to support user studies, providing a feasible approach and valuable insights for designing air taxi UX in the early design phase.
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation
By combining the reasoning capabilities of LLMs with the effectiveness of tree search, SeRTS boosts the zero-shot performance of retrieving high-quality and informative results for RAG.
How Well Can LLMs Echo Us? Evaluating AI Chatbots' Role-Play Ability with ECHO
However, existing studies focus on imitating well-known public figures or fictional characters, overlooking the potential for simulating ordinary individuals.
An Entropy-based Text Watermarking Detection Method
From the experiments, we demonstrate that our EWD can achieve better detection performance in low-entropy scenarios, and our method is also general and can be applied to texts with different entropy distributions.
Split to Merge: Unifying Separated Modalities for Unsupervised Domain Adaptation
In this work, we introduce a Unified Modality Separation (UniMoS) framework for unsupervised domain adaptation.
Agile Multi-Source-Free Domain Adaptation
Efficiently utilizing rich knowledge in pretrained models has become a critical topic in the era of large models.
CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models
Developing Large Language Models (LLMs) with robust long-context capabilities has been the recent research focus, resulting in the emergence of long-context LLMs proficient in Chinese.
Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation
Specifically, the image contextual information is utilized to prompt the language branch in a domain-agnostic and instance-conditioned way.
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown.
A Survey of Text Watermarking in the Era of Large Language Models
This paper conducts a comprehensive survey of the current state of text watermarking technology, covering four main aspects: (1) an overview and comparison of different text watermarking techniques; (2) evaluation methods for text watermarking algorithms, including their detectability, impact on text or LLM quality, robustness under target or untargeted attacks; (3) potential application scenarios for text watermarking technology; (4) current challenges and future directions for text watermarking.
Order-preserving Consistency Regularization for Domain Adaptation and Generalization
To alleviate this problem, data augmentation coupled with consistency regularization are commonly adopted to make the model less sensitive to domain-specific attributes.
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
With the exponential surge in diverse multi-modal data, traditional uni-modal retrieval methods struggle to meet the needs of users seeking access to data across various modalities.
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
Zero-Shot Learning by Harnessing Adversarial Samples
To take the advantage of image augmentations while mitigating the semantic distortion issue, we propose a novel ZSL approach by Harnessing Adversarial Samples (HAS).
VOLTA: Improving Generative Diversity by Variational Mutual Information Maximizing Autoencoder
The natural language generation domain has witnessed great success thanks to Transformer models.
Att-KGCN: Tourist Attractions Recommendation System by using Attention mechanism and Knowledge Graph Convolution Network
A knowledge graph dataset of tourist attractions used based on tourism data on Socotra Island-Yemen.
Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B).
Imbalanced Open Set Domain Adaptation via Moving-threshold Estimation and Gradual Alignment
To alleviate the negative effects raised by label shift in OSDA, we propose Open-set Moving-threshold Estimation and Gradual Alignment (OMEGA) - a novel architecture that improves existing OSDA methods on class-imbalanced data.
A Comprehensive Survey on Source-free Domain Adaptation
Over the past decade, domain adaptation has become a widely studied branch of transfer learning that aims to improve performance on target domains by leveraging knowledge from the source domain.
Multispectral Video Semantic Segmentation: A Benchmark Dataset and Baseline
This gives rise to significantly more robust segmentation of image objects in complex scenes and under adverse conditions.
Semantic Enhanced Knowledge Graph for Large-Scale Zero-Shot Learning
In this paper, we provide a new semantic enhanced knowledge graph that contains both expert knowledge and categories semantic correlation.
Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks
On the algorithmic side, we derive a new algorithm for black-box targeted attacks based on our theoretical analysis, in which we additionally minimize the maximum model discrepancy(M3D) of the substitute models when training the generator to generate adversarial examples.
How a Brand's Social Activism Impacts Consumers' Brand Evaluations: The Role of Brand Relationship Norms
These findings are attributable to consumers' perceptions of whether the brand's response strategy complies with relationship norms during social activism.
Variational Model Perturbation for Source-Free Domain Adaptation
Our model perturbation provides a new probabilistic way for domain adaptation which enables efficient adaptation to target domains while maximally preserving knowledge in source models.
Federated Zero-Shot Learning for Visual Recognition
We identify two key challenges in our FedZSL protocol: 1) the trained models are prone to be biased to the locally observed classes, thus failing to generalize to the unseen classes and/or seen classes appeared on other devices; 2) as each category in the training data comes from a single source, the central model is highly vulnerable to model replacement (backdoor) attacks.
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation.
Graph Component Contrastive Learning for Concept Relatedness Estimation
As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph.
Promoting Saliency From Depth: Deep Unsupervised RGB-D Saliency Detection
The laborious and time-consuming manual annotation has become a real bottleneck in various practical scenarios.
Text Revision by On-the-Fly Representation Optimization
In this approach, we simply fine-tune a pre-trained Transformer with masked language modeling and attribute classification.
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model
Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns.
Distinguishing Unseen From Seen for Generalized Zero-Shot Learning
In this paper, we present a novel method which leverages both visual and semantic modalities to distinguish seen and unseen categories.
Exploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Inspired by recent success in unsupervised contrastive representation learning, we propose a novel denoised cross-video contrastive algorithm, aiming to enhance the feature discrimination ability of video snippets for accurate temporal action localization in the weakly-supervised setting.
Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection
As a by-product, a CapS dataset is constructed by augmenting existing benchmark training set with additional image tags and captions.
Region Semantically Aligned Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes based on the knowledge of seen classes.
Domain Adaptive Semantic Segmentation without Source Data
To address these problems, we investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore.
Test-time Batch Statistics Calibration for Covariate Shift
An previous solution is test time normalization, which substitutes the source statistics in BN layers with the target batch statistics.
Adversarial Energy Disaggregation for Non-intrusive Load Monitoring
Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions, which is a typical application of data analysis.
Spectrum Gaussian Processes Based On Tunable Basis Functions
Spectral approximation and variational inducing learning for the Gaussian process are two popular methods to reduce computational complexity.
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e. g., attributes) to recognize the seen and unseen samples, where unseen classes are not observable during training.
A convolutional neural network for prestack fracture detection
From prestack seismic gathers, anisotropic analysis and inversion were commonly applied to characterize the dominant orientations and relative intensities of fractures.
Exploiting Cross-Session Information for Session-based Recommendation with Graph Neural Networks
Different from the traditional recommender system, the session-based recommender system introduces the concept of the session, i. e., a sequence of interactions between a user and multiple items within a period, to preserve the user's recent interest.
Calibrated RGB-D Salient Object Detection
Complex backgrounds and similar appearances between objects and their surroundings are generally recognized as challenging scenarios in Salient Object Detection (SOD).
Ranked #3 on
Object Detection
on PKU-DDD17-Car
Learning Calibrated Medical Image Segmentation via Multi-Rater Agreement Modeling
To our knowledge, our work is the first in producing calibrated predictions under different expertise levels for medical image segmentation.
Multi-Stage Aggregated Transformer Network for Temporal Language Localization in Videos
Then for a moment candidate, we concatenate the starting/middle/ending representations of its starting/middle/ending elements respectively to form the final moment representation.
Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
Previous bi-classifier adversarial learning methods only focus on the similarity between the outputs of two distinct classifiers.
Dual MINE-based Neural Secure Communications under Gaussian Wiretap Channel
The security constraints of this method is constructed only with the input and output signal samples of the legal and eavesdropper channels and benefit that training the encoder is completely independent of the decoder.
Open-Retrieval Conversational Machine Reading
On our created OR-ShARC dataset, MUDERN achieves the state-of-the-art performance, outperforming existing single-passage conversational machine reading models as well as a new multi-passage conversational machine reading baseline by a large margin.
Semantics Disentangling for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training.
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes.
BV-Person: A Large-Scale Dataset for Bird-View Person Re-Identification
In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i. e., an elevated view of an object from above.
Dynamic Context-Sensitive Filtering Network for Video Salient Object Detection
Our bidirectional dynamic fusion strategy encourages the interaction of spatial and temporal information in a dynamic manner.
Ranked #15 on
Video Polyp Segmentation
on SUN-SEG-Easy (Unseen)
Deep Pairwise Hashing for Cold-start Recommendation
Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation
Such three strategies are formulated into a unified framework to address the fairness issue and domain shift challenge.
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Based on the learned EDU and entailment representations, we either reply to the user our final decision "yes/no/irrelevant" of the initial question, or generate a follow-up question to inquiry more information.
Rethinking Generative Zero-Shot Learning: An Ensemble Learning Perspective for Recognising Visual Patches
A voting strategy averages the probability distributions output from the classifiers and, given that some patches are more discriminative than others, a discrimination-based attention mechanism helps to weight each patch accordingly.
Accurate RGB-D Salient Object Detection via Collaborative Learning
The explicitly extracted edge information goes together with saliency to give more emphasis to the salient regions and object boundaries.
Ranked #20 on
RGB-D Salient Object Detection
on NJU2K
Unsupervised Text Generation by Learning from Search
In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search.
Dual-level Semantic Transfer Deep Hashing for Efficient Social Image Retrieval
Specifically, we design a complementary dual-level semantic transfer mechanism to efficiently discover the potential semantics of tags and seamlessly transfer them into binary hash codes.
Hybrid-DNNs: Hybrid Deep Neural Networks for Mixed Inputs
Regarding as a combination of feature learning and target learning, the new proposed networks provide great capacity in high-hierarchy feature extraction and in-depth data mining.
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations.
Memory-oriented Decoder for Light Field Salient Object Detection
In this paper, we present a deep-learning-based method where a novel memory-oriented decoder is tailored for light field saliency detection.
Rethinking the Item Order in Session-based Recommendation with Graph Neural Networks
In this paper, therefore, we study the item transition pattern by constructing a session graph and propose a novel model which collaboratively considers the sequence order and the latent order in the session graph for a session-based recommender system.
In Vitro Fertilization (IVF) Cumulative Pregnancy Rate Prediction from Basic Patient Characteristics
Tens of millions of women suffer from infertility worldwide each year.
Improving Question Generation With to the Point Context
Question generation (QG) is the task of generating a question from a reference sentence and a specified answer within the sentence.
CANZSL: Cycle-Consistent Adversarial Networks for Zero-Shot Learning from Natural Language
Thus, a multi-modal cycle-consistency loss between the synthesized semantic representations and the ground truth can be learned and leveraged to enforce the generated semantic features to approximate to the real distribution in semantic space.
Alleviating Feature Confusion for Generative Zero-shot Learning
An inevitable issue of such a paradigm is that the synthesized unseen features are prone to seen references and incapable to reflect the novelty and diversity of real unseen instances.
Cycle-consistent Conditional Adversarial Transfer Networks
Domain adaptation investigates the problem of cross-domain knowledge transfer where the labeled source domain and unlabeled target domain have distinctive data distributions.
Ranked #4 on
Domain Adaptation
on USPS-to-MNIST
SpatialNLI: A Spatial Domain Natural Language Interface to Databases Using Spatial Comprehension
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS).
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation
Visual paragraph generation aims to automatically describe a given image from different perspectives and organize sentences in a coherent way.
Agile Domain Adaptation
Because of the distribution shifts, different target samples have distinct degrees of difficulty in adaptation.
From Zero-Shot Learning to Cold-Start Recommendation
This work, for the first time, formulates CSR as a ZSL problem, and a tailor-made ZSL method is proposed to handle CSR.
A Fusion Adversarial Underwater Image Enhancement Network with a Public Test Dataset
Underwater image enhancement algorithms have attracted much attention in underwater vision task.
Coupled VAE: Improved Accuracy and Robustness of a Variational Autoencoder
We analyze the histogram of the likelihoods of the input images using the generalized mean, which measures the model's accuracy as a function of the relative risk.
Adaptive Collaborative Similarity Learning for Unsupervised Multi-view Feature Selection
We propose to dynamically learn the collaborative similarity structure, and further integrate it with the ultimate feature selection into a unified framework.
Discrete Optimal Graph Clustering
2) the relaxing process of cluster labels may cause significant information loss.
Leveraging the Invariant Side of Generative Zero-Shot Learning
In this paper, we take the advantage of generative adversarial networks (GANs) and propose a novel method, named leveraging invariant side GAN (LisGAN), which can directly generate the unseen features from random noises which are conditioned by the semantic descriptions.
Ranked #5 on
Generalized Zero-Shot Learning
on SUN Attribute
