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Found 24 papers, 10 papers with code
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model
APIRecX can migrate the knowledge of existing libraries to a new library, and can recommend APIs that are previously regarded as OOV.
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models
In this paper, we propose a mixture model-based end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL).
Constituents Correspond to Word Sequence Patterns among Sentences with Equivalent Predicate-Argument Structures: Unsupervised Constituency Parsing by Span Matching
In this study, we empirically verify that \textbf{constituents correspond to word sequence patterns in the PAS-equivalent sentence set}.
Meta-Point Learning and Refining for Category-Agnostic Pose Estimation
Existing methods only rely on the features extracted at support keypoints to predict or refine the keypoints on query image, but a few support feature vectors are local and inadequate for CAPE.
A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models
Specifically, we target the widely-used application scenario of image classification, and utilized three different datasets and five commonly-used performance metrics to assess in total 13 methods from diverse categories.
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation
To alleviate the expensive human labeling, semi-supervised semantic segmentation employs a few labeled images and an abundant of unlabeled images to predict the pixel-level label map with the same size.
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits
Our proposed method is significantly superior in terms of UAR and F1 to the single-task and multi-task baselines with p-values < 0. 05.
How to Evaluate Semantic Communications for Images with ViTScore Metric?
To evaluate the performance of ViTScore, we compare ViTScore with 3 typical metrics (PSNR, MS-SSIM, and LPIPS) through 4 classes of experiments: (i) correlation with BERTScore through evaluation of image caption downstream CV task, (ii) evaluation in classical image communications, (iii) evaluation in image semantic communication systems, and (iv) evaluation in image semantic communication systems with semantic attack.
Bias Testing and Mitigation in LLM-based Code Generation
To mitigate bias for code generation models, we evaluate five bias mitigation prompt strategies, i. e., utilizing bias testing results to refine the code (zero-shot), one-, few-shot, and two Chain-of-Thought (CoT) prompts.
Try with Simpler -- An Evaluation of Improved Principal Component Analysis in Log-based Anomaly Detection
This addresses the issue of unseen log events in training data, enhancing log representation.
Scene-aware Human Pose Generation using Transformer
Affordance learning considers the interaction opportunities for an actor in the scene and thus has wide application in scene understanding and intelligent robotics.
Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation
Specifically, we employ an asymmetric encoder to learn the compensating features of the RGB and the thermal images.
Ranked #17 on
Thermal Image Segmentation
on MFN Dataset
Generic and Robust Root Cause Localization for Multi-Dimensional Data in Online Service Systems
Such attribute combinations are substantial clues to the underlying root causes and thus are called root causes of multidimensional data.
iEnhancer-ELM: improve enhancer identification by extracting position-related multiscale contextual information based on enhancer language models
Although many feature extraction methods have been proposed to improve the performance of enhancer identification, they cannot learn position-related multiscale contextual information from raw DNA sequences.
Weak-shot Semantic Segmentation via Dual Similarity Transfer
Proposal segmentation allows proposal-pixel similarity transfer from base classes to novel classes, which enables the mask learning of novel classes.
Bridging the View Disparity Between Radar and Camera Features for Multi-modal Fusion 3D Object Detection
A novel method that realizes the feature-level fusion under the bird's-eye view (BEV) for a better feature representation is proposed.
Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity
Specifically, the ability of using mask prior to help detect objects is learned from base categories and transferred to novel categories.
Pyramid Correlation based Deep Hough Voting for Visual Object Tracking
Through experiments we find that, without regression, the performance could be equally promising as long as we delicately design the network to suit the training objective.
An Improved StarGAN for Emotional Voice Conversion: Enhancing Voice Quality and Data Augmentation
Emotional Voice Conversion (EVC) aims to convert the emotional style of a source speech signal to a target style while preserving its content and speaker identity information.
On the Evaluation of Neural Code Summarization
To achieve a profound understanding of how far we are from solving this problem and provide suggestions to future research, in this paper, we conduct a systematic and in-depth analysis of 5 state-of-the-art neural code summarization models on 6 widely used BLEU variants, 4 pre-processing operations and their combinations, and 3 widely used datasets.
Weak-shot Fine-grained Classification via Similarity Transfer
In this setting, we propose a method called SimTrans to transfer pairwise semantic similarity from base categories to novel categories.
A System for Worldwide COVID-19 Information Aggregation
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education.
Detection of Information Hiding at Anti-Copying 2D Barcodes
Our experimental results show that the PVBD scheme can correctly detect the existence of the hidden information at both the 2LQR code and the LCAC 2D barcode.
Learning from Web Data with Self-Organizing Memory Module
However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively.
Ranked #14 on
Image Classification
on WebVision-1000
