Search Results for author: Yunhe Feng

Found 11 papers, 5 papers with code

EmojiCloud: a Tool for Emoji Cloud Visualization

no code implementations NAACL (Emoji) 2022 Yunhe Feng, Cheng Guo, Bingbing Wen, Peng Sun, Yufei Yue, Dingwen Tao

This paper proposes EmojiCloud, an open-source Python-based emoji cloud visualization tool, to generate a quick and straightforward understanding of emojis from the perspective of frequency and importance.

Benchmarking the Robustness of UAV Tracking Against Common Corruptions

1 code implementation18 Mar 2024 Xiaoqiong Liu, Yunhe Feng, Shu Hu, Xiaohui Yuan, Heng Fan

Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions.


S3LLM: Large-Scale Scientific Software Understanding with LLMs using Source, Metadata, and Document

1 code implementation15 Mar 2024 Kareem Shaik, Dali Wang, Weijian Zheng, Qinglei Cao, Heng Fan, Peter Schwartz, Yunhe Feng

S3LLM demonstrates the potential of using locally deployed open-source LLMs for the rapid understanding of large-scale scientific computing software, eliminating the need for extensive coding expertise, and thereby making the process more efficient and effective.

Natural Language Queries

Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning

1 code implementation1 Dec 2023 Shaohua Dong, Yunhe Feng, Qing Yang, Yan Huang, Dongfang Liu, Heng Fan

Existing approaches often fully fine-tune a dual-branch encoder-decoder framework with a complicated feature fusion strategy for achieving multimodal semantic segmentation, which is training-costly due to the massive parameter updates in feature extraction and fusion.

Ranked #2 on Semantic Segmentation on SUN-RGBD (using extra training data)

Decoder object-detection +7

Addressing Weak Decision Boundaries in Image Classification by Leveraging Web Search and Generative Models

no code implementations30 Oct 2023 Preetam Prabhu Srikar Dammu, Yunhe Feng, Chirag Shah

Our new method is able to (1) identify weak decision boundaries for such classes; (2) construct search queries for Google as well as text for generating images through DALL-E 2 and Stable Diffusion; and (3) show how these newly captured training samples could alleviate population bias issue.

Image Classification

Towards Transferable Targeted Adversarial Examples

1 code implementation CVPR 2023 Zhibo Wang, Hongshan Yang, Yunhe Feng, Peng Sun, Hengchang Guo, Zhifei Zhang, Kui Ren

In this paper, we propose the Transferable Targeted Adversarial Attack (TTAA), which can capture the distribution information of the target class from both label-wise and feature-wise perspectives, to generate highly transferable targeted adversarial examples.

Adversarial Attack

Towards Generating Robust, Fair, and Emotion-Aware Explanations for Recommender Systems

no code implementations17 Aug 2022 Bingbing Wen, Yunhe Feng, Yongfeng Zhang, Chirag Shah

Current explanation generation models are found to exaggerate certain emotions without accurately capturing the underlying tone or the meaning.

Explainable Recommendation Explanation Generation +3

COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression

1 code implementation18 Nov 2021 Sian Jin, Chengming Zhang, Xintong Jiang, Yunhe Feng, Hui Guan, Guanpeng Li, Shuaiwen Leon Song, Dingwen Tao

In this paper, we propose a novel memory-efficient CNN training framework (called COMET) that leverages error-bounded lossy compression to significantly reduce the memory requirement for training, to allow training larger models or to accelerate training.

Data Compression

Seed Stocking Via Multi-Task Learning

no code implementations12 Jan 2021 Yunhe Feng, Wenjun Zhou

First, we will estimate the yield and risk of each variety as if they were planted at each location.

Multi-Task Learning

University of Washington at TREC 2020 Fairness Ranking Track

no code implementations3 Nov 2020 Yunhe Feng, Daniel Saelid, Ke Li, Ruoyuan Gao, Chirag Shah

The results showed that our runs performed below par for re-ranking task, but above average for retrieval.

Ethics Fairness +2

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