Search Results for author:
Found 16 papers, 8 papers with code
SQL-o1: A Self-Reward Heuristic Dynamic Search Method for Text-to-SQL
To address these issues, we propose SQL-o1, a Self-Reward-based heuristic search method designed to enhance the reasoning ability of LLMs in SQL query generation.
HGTUL: A Hypergraph-based Model For Trajectory User Linking
Moreover, we design a data balancing method to effectively address the user class imbalance problem and experimentally validate its significance in TUL.
Deep Reversible Consistency Learning for Cross-modal Retrieval
Then, RSC employs a Modality-invariant Representation Recasting mechanism (MRR) to recast the potential modality-invariant representations from sample semantic labels by the generalized inverse matrix of the prior.
Robust Self-Paced Hashing for Cross-Modal Retrieval with Noisy Labels
Inspired by human cognitive learning, a few methods introduce self-paced learning (SPL) to gradually train the model from easy to hard samples, which is often used to mitigate the effects of feature noise or outliers.
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
To address this challenge, we propose a novel RObust mUltitask Tuning and collaboration mEthod (ROUTE) to improve the comprehensive capabilities of open-source LLMs for Text2SQL, thereby providing a more practical solution.
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC).
PointCloud-Text Matching: Benchmark Datasets and a Baseline
We observe that the data is challenging and with noisy correspondence due to the sparsity, noise, or disorder of point clouds and the ambiguity, vagueness, or incompleteness of texts, which make existing cross-modal matching methods ineffective for PTM.
Cross-modal Active Complementary Learning with Self-refining Correspondence
Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities.
Cross-modal retrieval with noisy correspondence
Image-text matching
+1
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query.
Ranked #1 on
Text-based Person Retrieval with Noisy Correspondence
on ICFG-PEDES
(using extra training data)
EVIL: Evidential Inference Learning for Trustworthy Semi-supervised Medical Image Segmentation
Recently, uncertainty-aware methods have attracted increasing attention in semi-supervised medical image segmentation.
Deep Evidential Learning with Noisy Correspondence for Cross-Modal Retrieval
However, it will unavoidably introduce noise (i. e., mismatched pairs) into training data, dubbed noisy correspondence.
Ranked #2 on
Text-based Person Retrieval with Noisy Correspondence
on RSTPReid
(using extra training data)
Cross-modal retrieval with noisy correspondence
Retrieval
+1
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Effect of E-cigarette Use and Social Network on Smoking Behavior Change: An agent-based model of E-cigarette and Cigarette Interaction
To address these challenges, we built an agent-based model (ABM) of smoking and ECig use to examine the effects of ECig use on smoking behavior change.
Incorporating Label Dependency for Answer Quality Tagging in Community Question Answering via CNN-LSTM-CRF
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
