Zobrazeno 1 - 10
of 429
pro vyhledávání: '"Kot, Alex C"'
Autor:
Yu, Yi, Wang, Yufei, Yang, Wenhan, Guo, Lanqing, Lu, Shijian, Duan, Ling-Yu, Tan, Yap-Peng, Kot, Alex C.
Recent advancements in deep learning-based compression techniques have surpassed traditional methods. However, deep neural networks remain vulnerable to backdoor attacks, where pre-defined triggers induce malicious behaviors. This paper introduces a
Externí odkaz:
http://arxiv.org/abs/2412.01646
Given a natural language query, video moment retrieval aims to localize the described temporal moment in an untrimmed video. A major challenge of this task is its heavy dependence on labor-intensive annotations for training. Unlike existing works tha
Externí odkaz:
http://arxiv.org/abs/2412.00811
Autor:
Bao, Peijun, Kot, Alex C.
This paper presents SimBase, a simple yet effective baseline for temporal video grounding. While recent advances in temporal grounding have led to impressive performance, they have also driven network architectures toward greater complexity, with a r
Externí odkaz:
http://arxiv.org/abs/2411.07945
Person Re-identification (Person ReID) has advanced significantly in fully supervised and domain generalized Person R e ID. However, methods developed for one task domain transfer poorly to the other. An ideal Person ReID method should be effective r
Externí odkaz:
http://arxiv.org/abs/2410.08466
Supervised Person Re-identification (Person ReID) methods have achieved excellent performance when training and testing within one camera network. However, they usually suffer from considerable performance degradation when applied to different camera
Externí odkaz:
http://arxiv.org/abs/2410.08456
Person Re-identification (Person ReID) has progressed to a level where single-domain supervised Person ReID performance has saturated. However, such methods experience a significant drop in performance when trained and tested across different dataset
Externí odkaz:
http://arxiv.org/abs/2410.08460
Visible and Infrared Image Fusion (VIF) has garnered significant interest across a wide range of high-level vision tasks, such as object detection and semantic segmentation. However, the evaluation of VIF methods remains challenging due to the absenc
Externí odkaz:
http://arxiv.org/abs/2410.06811
Autor:
Kong, Chenqi, Luo, Anwei, Bao, Peijun, Li, Haoliang, Wan, Renjie, Zheng, Zengwei, Rocha, Anderson, Kot, Alex C.
Open-set face forgery detection poses significant security threats and presents substantial challenges for existing detection models. These detectors primarily have two limitations: they cannot generalize across unknown forgery domains and inefficien
Externí odkaz:
http://arxiv.org/abs/2408.12791
Autor:
Guo, Laniqng, Wang, Chong, Wang, Yufei, Yu, Yi, Huang, Siyu, Yang, Wenhan, Kot, Alex C., Wen, Bihan
Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions. {Comparing to other image restoration tasks, there are two unique chal
Externí odkaz:
http://arxiv.org/abs/2407.08865
Ensuring data privacy and protection has become paramount in the era of deep learning. Unlearnable examples are proposed to mislead the deep learning models and prevent data from unauthorized exploration by adding small perturbations to data. However
Externí odkaz:
http://arxiv.org/abs/2406.17349