Zobrazeno 1 - 10
of 3 334
pro vyhledávání: '"Yang, ZhiYong"'
Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, Jo
Externí odkaz:
http://arxiv.org/abs/2412.07499
Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover
Externí odkaz:
http://arxiv.org/abs/2410.06719
Autor:
Han, Boyu, Xu, Qianqian, Yang, Zhiyong, Bao, Shilong, Wen, Peisong, Jiang, Yangbangyan, Huang, Qingming
The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions.
Externí odkaz:
http://arxiv.org/abs/2409.20398
Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and collaborative filtering. Following the convention of RS, existing practices exploit unique user r
Externí odkaz:
http://arxiv.org/abs/2409.01012
Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Autor:
Wang, Zitai, Xu, Qianqian, Yang, Zhiyong, Wen, Peisong, He, Yuan, Cao, Xiaochun, Huang, Qingming
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have proposed various measures to evaluate model performances. However, bot
Externí odkaz:
http://arxiv.org/abs/2407.06709
Autor:
Li, Feiran, Xu, Qianqian, Bao, Shilong, Yang, Zhiyong, Cong, Runmin, Cao, Xiaochun, Huang, Qingming
This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects ar
Externí odkaz:
http://arxiv.org/abs/2405.09782
This paper explores a novel multi-modal alternating learning paradigm pursuing a reconciliation between the exploitation of uni-modal features and the exploration of cross-modal interactions. This is motivated by the fact that current paradigms of mu
Externí odkaz:
http://arxiv.org/abs/2405.09321
Autor:
Yang, Zhiyong, Xu, Qianqian, Wang, Zitai, Li, Sicong, Han, Boyu, Bao, Shilong, Cao, Xiaochun, Huang, Qingming
This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the variation in these distributions can be broken down hierarchically into
Externí odkaz:
http://arxiv.org/abs/2405.07780
The Area Under the ROC Curve (AUC) is a widely employed metric in long-tailed classification scenarios. Nevertheless, most existing methods primarily assume that training and testing examples are drawn i.i.d. from the same distribution, which is ofte
Externí odkaz:
http://arxiv.org/abs/2311.03055
Autor:
Gao, Peifeng, Xu, Qianqian, Yang, Yibo, Wen, Peisong, Shao, Huiyang, Yang, Zhiyong, Ghanem, Bernard, Huang, Qingming
Neural Collapse (NC) is a well-known phenomenon of deep neural networks in the terminal phase of training (TPT). It is characterized by the collapse of features and classifier into a symmetrical structure, known as simplex equiangular tight frame (ET
Externí odkaz:
http://arxiv.org/abs/2310.08358