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.
no code implementations • 27 May 2024 • Shaohua Dong, Yunhe Feng, Qing Yang, Yuewei Lin, Heng Fan
In this paper, we aim to mitigate such information loss to boost the performance of the low-resolution Transformer tracking via dual knowledge distillation from a frozen high-resolution (but not a larger) Transformer tracker.
1 code implementation • 18 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.
1 code implementation • 15 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.
1 code implementation • 1 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 #4 on Semantic Segmentation on SUN-RGBD (using extra training data)
no code implementations • 30 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.
no code implementations • ICCV 2023 • Lei Zhang, Zhibo Wang, Xiaowei Dong, Yunhe Feng, Xiaoyi Pang, Zhifei Zhang, Kui Ren
Network pruning aims to compress models while minimizing loss in accuracy.
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.
no code implementations • 17 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.
1 code implementation • 18 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.
no code implementations • 12 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.
no code implementations • 3 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.