4.5k citations · 4.6k across the 16 of their papers we have counts for
7 papers · 1 filter
Prompting DirectSAM for Semantic Contour Extraction in Remote Sensing Images
Shiyu Miao, Delong Chen, Fan Liu +4
The Direct Segment Anything Model (DirectSAM) excels in class-agnostic contour extraction. In this paper, we explore its use by applying it to optical remote sensing imagery, where…
Making Large Vision Language Models to be Good Few-shot Learners
Fan Liu, Wenwen Cai, Jian Huo +3
Few-shot classification (FSC) is a fundamental yet challenging task in computer vision that involves recognizing novel classes from limited data. While previous methods have focuse…
What Makes for Good Image Captions?
Delong Chen, Samuel Cahyawijaya, Etsuko Ishii +3
This paper establishes a formal information-theoretic framework for image captioning, conceptualizing captions as compressed linguistic representations that selectively encode sema…
High-Dimension Human Value Representation in Large Language Models
Samuel Cahyawijaya, Delong Chen, Yejin Bang +5
The widespread application of LLMs across various tasks and fields has necessitated the alignment of these models with human values and preferences. Given various approaches of hum…
Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee +1
We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus o…
Subobject-level Image Tokenization
Delong Chen, Samuel Cahyawijaya, Jianfeng Liu +2
Patch-based image tokenization ignores the morphology of the visual world, limiting effective and efficient learning of image understanding. Inspired by subword tokenization, we in…
Few-shot Adaptation of Multi-modal Foundation Models: A Survey
Fan Liu, Tianshu Zhang, Wenwen Dai +3
Multi-modal (vision-language) models, such as CLIP, are replacing traditional supervised pre-training models (e.g., ImageNet-based pre-training) as the new generation of visual fou…