Zobrazeno 1 - 10
of 5 054
pro vyhledávání: '"Fan, Xin"'
Autor:
Zhang, Yuxiang, Fan, Xin, Wang, Junjie, Chen, Chongxian, Mo, Fan, Sakai, Tetsuya, Yamana, Hayato
Recent advancements in large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. Despite this progress, the vast scale of tool retrieval remains challen
Externí odkaz:
http://arxiv.org/abs/2410.03212
Transfer attacks generate significant interest for real-world black-box applications by crafting transferable adversarial examples through surrogate models. Whereas, existing works essentially directly optimize the single-level objective w.r.t. the s
Externí odkaz:
http://arxiv.org/abs/2406.02064
Graphs play an increasingly important role in various big data applications. However, existing graph data structures cannot simultaneously address the performance bottlenecks caused by the dynamic updates, large scale, and high query complexity of cu
Externí odkaz:
http://arxiv.org/abs/2405.15193
Attribute and object (A-O) disentanglement is a fundamental and critical problem for Compositional Zero-shot Learning (CZSL), whose aim is to recognize novel A-O compositions based on foregone knowledge. Existing methods based on disentangled represe
Externí odkaz:
http://arxiv.org/abs/2403.05924
Image stitching seamlessly integrates images captured from varying perspectives into a single wide field-of-view image. Such integration not only broadens the captured scene but also augments holistic perception in computer vision applications. Given
Externí odkaz:
http://arxiv.org/abs/2402.15959
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fus
Externí odkaz:
http://arxiv.org/abs/2401.00421
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications. However, prevailing AT methods, relying on direct iterative updates for target model's defense, frequently enco
Externí odkaz:
http://arxiv.org/abs/2310.12713
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination and low signal-to-noise ratio in dark environments, a multitude of pro
Externí odkaz:
http://arxiv.org/abs/2309.05267