6 papers · 1 filter
TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query over Time-Series Databases
Fei Lyu, Zhiyi Peng, Jiaming Liu +5
Large language models (LLMs) have significantly advanced natural language querying over relational databases, yet their ability to query time-series databases (TSDBs) remains large…
Semantically Compatible Knowledge Distillation for Cross-Domain Object Detection with Vision Foundation Models
Qifeng Zhang, Ting Xiang, Zeyuan Bai +1
Vision foundation models (VFMs) offer strong generalization capabilities for domain-adaptive object detection (DAOD). However, existing VFM-based methods overlook the spatial-scale…
Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Ting Xiang, Chenxi Deng, Jinhui Zhao +4
Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effec…
Human-Guided Causal Knowledge Injection for Virtual Cells
Pengcheng Wang, Changjian Chen, Zhuo Tang +4
Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecti…
CausalTAD: Injecting Causal Knowledge into Large Language Models for Tabular Anomaly Detection
Ruiqi Wang, Ruikang Liu, Runyu Chen +4
Detecting anomalies in tabular data is critical for many real-world applications, such as credit card fraud detection. With the rapid advancements in large language models (LLMs),…
Invisible Clean-Label Backdoor Attacks for Generative Data Augmentation
Ting Xiang, Jinhui Zhao, Changjian Chen +1
With the rapid advancement of image generative models, generative data augmentation has become an effective way to enrich training images, especially when only small-scale datasets…