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Found 20 papers, 7 papers with code
Towards More Accurate Uncertainty Estimation In Text Classification
The uncertainty measurement of classified results is especially important in areas requiring limited human resources for higher accuracy.
Uncertainty-Aware Cross-Lingual Transfer with Pseudo Partial Labels
Large-scale multilingual pre-trained language models have achieved remarkable performance in zero-shot cross-lingual tasks.
Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges
Studies highlight the gap in the deceptive power of LLM-generated fake news with and without human assistance, yet the potential of prompting techniques has not been fully explored.
Semi-Supervised Dialogue Abstractive Summarization via High-Quality Pseudolabel Selection
Semi-supervised dialogue summarization (SSDS) leverages model-generated summaries to reduce reliance on human-labeled data and improve the performance of summarization models.
Abstractive Text Summarization
Natural Language Understanding
Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection
This serves as a reminder to carefully consider sensitivity and confidence in the pursuit of model fairness.
Can LLM find the green circle? Investigation and Human-guided tool manipulation for compositional generalization
The meaning of complex phrases in natural language is composed of their individual components.
Uncertainty Estimation on Sequential Labeling via Uncertainty Transmission
Sequential labeling is a task predicting labels for each token in a sequence, such as Named Entity Recognition (NER).
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics.
Ranked #1 on
Unsupervised Monocular Depth Estimation
on KITTI-C
(using extra training data)
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation.
TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation
Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance.
Zero-Shot End-to-End Spoken Language Understanding via Cross-Modal Selective Self-Training
End-to-end (E2E) spoken language understanding (SLU) is constrained by the cost of collecting speech-semantics pairs, especially when label domains change.
Natural Language Understanding
Spoken Language Understanding
SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence
The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts.
Ranked #2 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching
To deal with the above issues, we propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching, which jointly models the local consistency and scale variations in a unified coarse-to-fine architecture.
D2Former: Jointly Learning Hierarchical Detectors and Contextual Descriptors via Agent-Based Transformers
Second, the HKDL module can generate keypoint detectors in a hierarchical way, which is helpful for detecting keypoints with diverse levels of structures.
Foreground-Background Distribution Modeling Transformer for Visual Object Tracking
To address the above limitations, we propose a novel foreground-background distribution modeling transformer for visual object tracking (F-BDMTrack), including a fore-background agent learning (FBAL) module and a distribution-aware attention (DA2) module in a unified transformer architecture.
Query Refinement Transformer for 3D Instance Segmentation
Additionally, we design an affiliated transformer decoder that suppresses the interference of noise background queries and helps the foreground queries focus on instance discriminative parts to predict final segmentation results.
Ranked #3 on
3D Instance Segmentation
on ScanNet(v2)
LANS: Large-scale Arabic News Summarization Corpus
Yet, Arabic Text Summarization (ATS) is still in its developing stages.
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer
To address these issues, we propose a novel end-to-end Part-Aware Transformer (PAT) for occluded person Re-ID through diverse part discovery via a transformer encoderdecoder architecture, including a pixel context based transformer encoder and a part prototype based transformer decoder.
Semantic Editing On Segmentation Map Via Multi-Expansion Loss
Each MEx area has the mask area of the generation as the majority and the boundary of original context as the minority.
Few-Shot Semantic Segmentation Augmented with Image-Level Weak Annotations
Despite the great progress made by deep neural networks in the semantic segmentation task, traditional neural-networkbased methods typically suffer from a shortage of large amounts of pixel-level annotations.
