Zobrazeno 1 - 10
of 7 523
pro vyhledávání: '"WAN JUN"'
Large Vision-Language Models (LVLMs) excel in cross-model tasks but experience performance declines in long-context reasoning due to overreliance on textual information and reduced visual dependency. In this study, we empirically analyze LVLMs in lon
Externí odkaz:
http://arxiv.org/abs/2410.19732
Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separately due to their distinct characteristics. Thus, when being combined
Externí odkaz:
http://arxiv.org/abs/2408.12793
Autor:
Chen, Zhigang, Zhou, Benjia, Huang, Yiqing, Wan, Jun, Hu, Yibo, Shi, Hailin, Liang, Yanyan, Lei, Zhen, Zhang, Du
Sign Language Representation Learning (SLRL) is crucial for a range of sign language-related downstream tasks such as Sign Language Translation (SLT) and Sign Language Retrieval (SLRet). Recently, many gloss-based and gloss-free SLRL methods have bee
Externí odkaz:
http://arxiv.org/abs/2408.09949
Autor:
Zou, Hang, Du, Chenxi, Liu, Ajian, Zhang, Yuan, Liu, Jing, Yang, Mingchuan, Wan, Jun, Zhang, Hui
Iris recognition is widely used in high-security scenarios due to its stability and distinctiveness. However, the acquisition of iris images typically requires near-infrared illumination and near-infrared band filters, leading to significant and cons
Externí odkaz:
http://arxiv.org/abs/2408.09752
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous methods fail to effectively leverage the rich knowledge in language models
Externí odkaz:
http://arxiv.org/abs/2407.20920
In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, t
Externí odkaz:
http://arxiv.org/abs/2407.08216
Autor:
Bai, Long, Chen, Tong, Tan, Qiaozhi, Nah, Wan Jun, Li, Yanheng, He, Zhicheng, Yuan, Sishen, Chen, Zhen, Wu, Jinlin, Islam, Mobarakol, Li, Zhen, Liu, Hongbin, Ren, Hongliang
Wireless Capsule Endoscopy (WCE) is highly valued for its non-invasive and painless approach, though its effectiveness is compromised by uneven illumination from hardware constraints and complex internal dynamics, leading to overexposed or underexpos
Externí odkaz:
http://arxiv.org/abs/2406.13705
Autor:
Triantafillou, Eleni, Kairouz, Peter, Pedregosa, Fabian, Hayes, Jamie, Kurmanji, Meghdad, Zhao, Kairan, Dumoulin, Vincent, Junior, Julio Jacques, Mitliagkas, Ioannis, Wan, Jun, Hosoya, Lisheng Sun, Escalera, Sergio, Dziugaite, Gintare Karolina, Triantafillou, Peter, Guyon, Isabelle
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust evaluation methodologies. The competition was highly successful: nearl
Externí odkaz:
http://arxiv.org/abs/2406.09073
Continual learning (CL) aims to extend deep models from static and enclosed environments to dynamic and complex scenarios, enabling systems to continuously acquire new knowledge of novel categories without forgetting previously learned knowledge. Rec
Externí odkaz:
http://arxiv.org/abs/2405.11822
Class-incremental learning (CIL) has emerged as a means to learn new classes incrementally without catastrophic forgetting of previous classes. Recently, CIL has undergone a paradigm shift towards dynamic architectures due to their superior performan
Externí odkaz:
http://arxiv.org/abs/2405.08533