8 papers
Linear-Time Demonstration Selection for In-Context Learning via Gradient Estimation
Ziniu Zhang, Zhenshuo Zhang, Dongyue Li +3
This paper introduces an algorithm to select demonstration examples for in-context learning of a query set. Given a set of examples, how can we quickly select out of to…
Efficient Ensemble for Fine-tuning Language Models on Multiple Datasets
Dongyue Li, Ziniu Zhang, Lu Wang +1
This paper develops an ensemble method for fine-tuning a language model to multiple datasets. Existing methods, such as quantized LoRA (QLoRA), are efficient when adapting to a sin…
Split Matching for Inductive Zero-shot Semantic Segmentation
Jialei Chen, Xu Zheng, Dongyue Li +6
Zero-shot Semantic Segmentation (ZSS) aims to segment categories that are not annotated during training. While fine-tuning vision-language models has achieved promising results, th…
Not All Pixels Are Equal: Confidence-Guided Attention for Feature Matching
Dongyue Li
Semi-dense feature matching methods have been significantly advanced by leveraging attention mechanisms to extract discriminative descriptors. However, most existing approaches tre…
CQVPR: Landmark-aware Contextual Queries for Visual Place Recognition
Dongyue Li, Daisuke Deguchi, Hiroshi Murase
Visual Place Recognition (VPR) aims to estimate the location of the given query image within a database of geo-tagged images. To identify the exact location in an image, detecting…
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach
Dongyue Li, Ziniu Zhang, Lu Wang +1
We study the problem of fine-tuning a language model (LM) for a target task by optimally using the information from auxiliary tasks. This problem has broad applications in NLP,…