Search Results for author: Yi-Fan Zhang

Found 13 papers, 10 papers with code

Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models

1 code implementation12 Jun 2024 Yi-Fan Zhang, Qingsong Wen, Chaoyou Fu, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin

Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning.

Image Compression

Debiasing Multimodal Large Language Models

1 code implementation8 Mar 2024 Yi-Fan Zhang, Weichen Yu, Qingsong Wen, Xue Wang, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

In the realms of computer vision and natural language processing, Large Vision-Language Models (LVLMs) have become indispensable tools, proficient in generating textual descriptions based on visual inputs.

Fairness Question Answering

Assaying on the Robustness of Zero-Shot Machine-Generated Text Detectors

1 code implementation20 Dec 2023 Yi-Fan Zhang, Zhang Zhang, Liang Wang, Tieniu Tan, Rong Jin

In an effort to address these issues, we delve into the realm of zero-shot machine-generated text detection.

Binary Classification Text Detection +1

A Study on the Calibration of In-context Learning

1 code implementation7 Dec 2023 HANLIN ZHANG, Yi-Fan Zhang, Yaodong Yu, Dhruv Madeka, Dean Foster, Eric Xing, Himabindu Lakkaraju, Sham Kakade

Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern language models (LMs).

In-Context Learning Natural Language Understanding +1

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

1 code implementation NeurIPS 2023 Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.

Time Series Time Series Forecasting

AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation

1 code implementation25 Apr 2023 Yi-Fan Zhang, Xue Wang, Kexin Jin, Kun Yuan, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

In particular, when the adaptation target is a series of domains, the adaptation accuracy of AdaNPC is 50% higher than advanced TTA methods.

Domain Generalization Test-time Adaptation

Evaluating Step-by-Step Reasoning through Symbolic Verification

1 code implementation16 Dec 2022 Yi-Fan Zhang, HANLIN ZHANG, Li Erran Li, Eric Xing

Pre-trained language models (LMs) have shown remarkable reasoning performance using explanations or chain-of-thoughts (CoT)) for in-context learning.

In-Context Learning

Domain-Specific Risk Minimization for Out-of-Distribution Generalization

1 code implementation18 Aug 2022 Yi-Fan Zhang, Jindong Wang, Jian Liang, Zhang Zhang, Baosheng Yu, Liang Wang, DaCheng Tao, Xing Xie

Our bound motivates two strategies to reduce the gap: the first one is ensembling multiple classifiers to enrich the hypothesis space, then we propose effective gap estimation methods for guiding the selection of a better hypothesis for the target.

Domain Generalization Out-of-Distribution Generalization

Vision-Language Intelligence: Tasks, Representation Learning, and Large Models

no code implementations3 Mar 2022 Feng Li, Hao Zhang, Yi-Fan Zhang, Shilong Liu, Jian Guo, Lionel M. Ni, Pengchuan Zhang, Lei Zhang

This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension.

Few-Shot Learning Representation Learning

Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation

1 code implementation2 Feb 2022 Yi-Fan Zhang, HANLIN ZHANG, Zachary C. Lipton, Li Erran Li, Eric P. Xing

Previous works on Treatment Effect Estimation (TEE) are not in widespread use because they are predominantly theoretical, where strong parametric assumptions are made but untractable for practical application.

POS Selection bias

Towards Principled Disentanglement for Domain Generalization

1 code implementation CVPR 2022 HANLIN ZHANG, Yi-Fan Zhang, Weiyang Liu, Adrian Weller, Bernhard Schölkopf, Eric P. Xing

To tackle this challenge, we first formalize the OOD generalization problem as constrained optimization, called Disentanglement-constrained Domain Generalization (DDG).

Disentanglement Domain Generalization

Focal and Efficient IOU Loss for Accurate Bounding Box Regression

no code implementations20 Jan 2021 Yi-Fan Zhang, Weiqiang Ren, Zhang Zhang, Zhen Jia, Liang Wang, Tieniu Tan

(ii) Most of the loss functions ignore the imbalance problem in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR.

object-detection Object Detection +2

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