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pro vyhledávání: '"Yang Yongxin"'
Autor:
Tudosiu, Petru-Daniel, Yang, Yongxin, Zhang, Shifeng, Chen, Fei, McDonagh, Steven, Lampouras, Gerasimos, Iacobacci, Ignacio, Parisot, Sarah
Text-to-image generation has achieved astonishing results, yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering, scene layout conditioning, or
Externí odkaz:
http://arxiv.org/abs/2404.02790
Current vision large language models (VLLMs) exhibit remarkable capabilities yet are prone to generate harmful content and are vulnerable to even the simplest jailbreaking attacks. Our initial analysis finds that this is due to the presence of harmfu
Externí odkaz:
http://arxiv.org/abs/2402.02207
Although significant progress has been made in the field of 2D-based interactive editing, fine-grained 3D-based interactive editing remains relatively unexplored. This limitation can be attributed to two main challenges: the lack of an efficient 3D r
Externí odkaz:
http://arxiv.org/abs/2312.15856
The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications. Due to huge parametersizes and extensive running costs, companies or organisations tend to transfer the models to the
Externí odkaz:
http://arxiv.org/abs/2305.14973
Generative modelling over continuous-time geometric constructs, a.k.a such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of captu
Externí odkaz:
http://arxiv.org/abs/2304.03785
Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted cl
Externí odkaz:
http://arxiv.org/abs/2304.01830
Self-supervised pre-training, based on the pretext task of instance discrimination, has fueled the recent advance in label-efficient object detection. However, existing studies focus on pre-training only a feature extractor network to learn transfera
Externí odkaz:
http://arxiv.org/abs/2211.09022
Autor:
Dong, Qishi, Muhammad, Awais, Zhou, Fengwei, Xie, Chuanlong, Hu, Tianyang, Yang, Yongxin, Bae, Sung-Ho, Li, Zhenguo
Recent advances on large-scale pre-training have shown great potentials of leveraging a large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) generalization, for which the goal is to perform well on possible unseen domains af
Externí odkaz:
http://arxiv.org/abs/2210.09236
A multitude of work has shown that machine learning-based medical diagnosis systems can be biased against certain subgroups of people. This has motivated a growing number of bias mitigation algorithms that aim to address fairness issues in machine le
Externí odkaz:
http://arxiv.org/abs/2210.01725
Publikováno v:
2020 International Conference on 3D Vision (3DV), pp. 81-90. IEEE, 2020
Growing free online 3D shapes collections dictated research on 3D retrieval. Active debate has however been had on (i) what the best input modality is to trigger retrieval, and (ii) the ultimate usage scenario for such retrieval. In this paper, we of
Externí odkaz:
http://arxiv.org/abs/2209.10020