4.5k citations · 4.6k across the 16 of their papers we have counts for
16 papers
RemoteSAM: Towards Segment Anything for Earth Observation
Liang Yao, Fan Liu, Delong Chen +6
We aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets wh…
Chain-of-Talkers (CoTalk): Fast Human Annotation of Dense Image Captions
Yijun Shen, Delong Chen, Fan Liu +4
While densely annotated image captions significantly facilitate the learning of robust vision-language alignment, methodologies for systematically optimizing human annotation effor…
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…
Visual Instruction Tuning with Polite Flamingo
Delong Chen, Jianfeng Liu, Wenliang Dai +1
Recent research has demonstrated that the multi-task fine-tuning of multi-modal Large Language Models (LLMs) using an assortment of annotated downstream vision-language datasets si…
RemoteCLIP: A Vision Language Foundation Model for Remote Sensing
Fan Liu, Delong Chen, Zhangqingyun Guan +5
General-purpose foundation models have led to recent breakthroughs in artificial intelligence. In remote sensing, self-supervised learning (SSL) and Masked Image Modeling (MIM) hav…
Taming Diffusion Models for Music-driven Conducting Motion Generation
Zhuoran Zhao, Jinbin Bai, Delong Chen +2
Generating the motion of orchestral conductors from a given piece of symphony music is a challenging task since it requires a model to learn semantic music features and capture the…
Few-shot Classification via Ensemble Learning with Multi-Order Statistics
Sai Yang, Fan Liu, Delong Chen +1
Transfer learning has been widely adopted for few-shot classification. Recent studies reveal that obtaining good generalization representation of images on novel classes is the key…
ProtoCLIP: Prototypical Contrastive Language Image Pretraining
Delong Chen, Zhao Wu, Fan Liu +4
Contrastive Language Image Pretraining (CLIP) has received widespread attention, since its learned representations can be transferred well to various downstream tasks. During the t…
Survey of Hallucination in Natural Language Generation
Ziwei Ji, Nayeon Lee, Rita Frieske +10
Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based…
VirtualConductor: Music-driven Conducting Video Generation System
Delong Chen, Fan Liu, Zewen Li +1
In this demo, we present VirtualConductor, a system that can generate conducting video from any given music and a single user's image. First, a large-scale conductor motion dataset…