Search Results for author: Yunhui Guo

Found 19 papers, 10 papers with code

Inconsistency-Based Data-Centric Active Open-Set Annotation

1 code implementation10 Jan 2024 Ruiyu Mao, Ouyang Xu, Yunhui Guo

The presence of unknown classes in the data can significantly impact the performance of existing active learning methods due to the uncertainty they introduce.

Active Learning valid

Segment Every Out-of-Distribution Object

no code implementations27 Nov 2023 Wenjie Zhao, Jia Li, Xin Dong, Yu Xiang, Yunhui Guo

Semantic segmentation models, while effective for in-distribution categories, face challenges in real-world deployment due to encountering out-of-distribution (OoD) objects.

Object Segmentation +1

Towards Effective Semantic OOD Detection in Unseen Domains: A Domain Generalization Perspective

no code implementations18 Sep 2023 Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen

In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.

Domain Generalization

VEATIC: Video-based Emotion and Affect Tracking in Context Dataset

no code implementations13 Sep 2023 Zhihang Ren, Jefferson Ortega, Yifan Wang, Zhimin Chen, Yunhui Guo, Stella X. Yu, David Whitney

Along with the dataset, we propose a new computer vision task to infer the affect of the selected character via both context and character information in each video frame.

Audio-Visual Class-Incremental Learning

1 code implementation ICCV 2023 Weiguo Pian, Shentong Mo, Yunhui Guo, Yapeng Tian

We demonstrate that joint audio-visual modeling can improve class-incremental learning, but current methods fail to preserve semantic similarity between audio and visual features as incremental step grows.

Class Incremental Learning Incremental Learning +3

Unsupervised Feature Learning with Emergent Data-Driven Prototypicality

no code implementations4 Jul 2023 Yunhui Guo, Youren Zhang, Yubei Chen, Stella X. Yu

With our feature mapper simply trained to spread out training instances in hyperbolic space, we observe that images move closer to the origin with congealing, validating our idea of unsupervised prototypicality discovery.

Metric Learning

Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction

1 code implementation7 Feb 2023 Yangxiao Lu, Ninad Khargonkar, Zesheng Xu, Charles Averill, Kamalesh Palanisamy, Kaiyu Hang, Yunhui Guo, Nicholas Ruozzi, Yu Xiang

By applying multi-object tracking and video object segmentation on the images collected via robot pushing, our system can generate segmentation masks of all the objects in these images in a self-supervised way.

Multi-Object Tracking Object +6

SCALE: Online Self-Supervised Lifelong Learning without Prior Knowledge

1 code implementation24 Aug 2022 Xiaofan Yu, Yunhui Guo, Sicun Gao, Tajana Rosing

To address the challenges, we propose Self-Supervised ContrAstive Lifelong LEarning without Prior Knowledge (SCALE) which can extract and memorize representations on the fly purely from the data continuum.

Unsupervised Hierarchical Semantic Segmentation with Multiview Cosegmentation and Clustering Transformers

1 code implementation CVPR 2022 Tsung-Wei Ke, Jyh-Jing Hwang, Yunhui Guo, Xudong Wang, Stella X. Yu

We enforce spatial consistency of grouping and bootstrap feature learning with co-segmentation among multiple views of the same image, and enforce semantic consistency across the grouping hierarchy with clustering transformers between coarse- and fine-grained features.

Clustering Segmentation +1

Attacking Lifelong Learning Models with Gradient Reversion

no code implementations ICLR 2020 Yunhui Guo, Mingrui Liu, Yandong Li, Liqiang Wang, Tianbao Yang, Tajana Rosing

We evaluate the effectiveness of traditional attack methods such as FGSM and PGD. The results show that A-GEM still possesses strong continual learning ability in the presence of adversarial examples in the memory and simple defense techniques such as label smoothing can further alleviate the adversarial effects.

Continual Learning

A Broader Study of Cross-Domain Few-Shot Learning

2 code implementations ECCV 2020 Yunhui Guo, Noel C. Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, Rogerio Feris

Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning.

cross-domain few-shot learning Few-Shot Image Classification +1

AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning

no code implementations21 Nov 2019 Yunhui Guo, Yandong Li, Liqiang Wang, Tajana Rosing

Fine-tuning is a popular transfer learning technique for deep neural networks where a few rounds of training are applied to the parameters of a pre-trained model to adapt them to a new task.

General Classification Image Classification +1

Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient

no code implementations25 Sep 2019 Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

In this paper, we introduce a novel and effective lifelong learning algorithm, called MixEd stochastic GrAdient (MEGA), which allows deep neural networks to acquire the ability of retaining performance on old tasks while learning new tasks.

Improved Schemes for Episodic Memory-based Lifelong Learning

1 code implementation NeurIPS 2020 Yunhui Guo, Mingrui Liu, Tianbao Yang, Tajana Rosing

This view leads to two improved schemes for episodic memory based lifelong learning, called MEGA-I and MEGA-II.

Depthwise Convolution is All You Need for Learning Multiple Visual Domains

1 code implementation3 Feb 2019 Yunhui Guo, Yandong Li, Rogerio Feris, Liqiang Wang, Tajana Rosing

A model aware of the relationships between different domains can also be trained to work on new domains with less resources.

Continual Learning

SpotTune: Transfer Learning through Adaptive Fine-tuning

3 code implementations CVPR 2019 Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris

Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in computer vision.

Inductive Bias Transfer Learning

A Survey on Methods and Theories of Quantized Neural Networks

no code implementations13 Aug 2018 Yunhui Guo

For all its popularity, deep neural networks are also criticized for consuming a lot of memory and draining battery life of devices during training and inference.

Quantization speech-recognition +1

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