Zobrazeno 1 - 10
of 1 002
pro vyhledávání: '"Wu Yongjian"'
This paper makes a step towards modeling the modality discrepancy in the cross-spectral re-identification task. Based on the Lambertain model, we observe that the non-linear modality discrepancy mainly comes from diverse linear transformations acting
Externí odkaz:
http://arxiv.org/abs/2411.01225
Large-scale visual-language pre-trained models (VLPMs) have demonstrated exceptional performance in downstream object detection through text prompts for natural scenes. However, their application to zero-shot nuclei detection on histopathology images
Externí odkaz:
http://arxiv.org/abs/2410.16820
Extracting robust feature representation is critical for object re-identification to accurately identify objects across non-overlapping cameras. Although having a strong representation ability, the Vision Transformer (ViT) tends to overfit on most di
Externí odkaz:
http://arxiv.org/abs/2408.16684
Prompt tuning methods have achieved remarkable success in parameter-efficient fine-tuning on large pre-trained models. However, their application to dual-modal fusion-based visual-language pre-trained models (VLPMs), such as GLIP, has encountered iss
Externí odkaz:
http://arxiv.org/abs/2407.11414
This paper explores a novel dynamic network for vision and language tasks, where the inferring structure is customized on the fly for different inputs. Most previous state-of-the-art approaches are static and hand-crafted networks, which not only hea
Externí odkaz:
http://arxiv.org/abs/2406.00334
Autor:
Wu, Yongjian, Zhou, Yang, Saiyin, Jiya, Wei, Bingzheng, Lai, Maode, Shou, Jianzhong, Fan, Yubo, Xu, Yan
Large-scale visual-language pre-trained models (VLPM) have proven their excellent performance in downstream object detection for natural scenes. However, zero-shot nuclei detection on H\&E images via VLPMs remains underexplored. The large gap between
Externí odkaz:
http://arxiv.org/abs/2306.17659
Autor:
Zhou, Yang, Wu, Yongjian, Wang, Zihua, Wei, Bingzheng, Lai, Maode, Shou, Jianzhong, Fan, Yubo, Xu, Yan
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-consuming and laborious for th
Externí odkaz:
http://arxiv.org/abs/2306.02691
Deep neural networks have been applied in many computer vision tasks and achieved state-of-the-art performance. However, misclassification will occur when DNN predicts adversarial examples which add human-imperceptible adversarial noise to natural ex
Externí odkaz:
http://arxiv.org/abs/2303.16697
Adversarial training can improve the robustness of neural networks. Previous methods focus on a single adversarial training strategy and do not consider the model property trained by different strategies. By revisiting the previous methods, we find d
Externí odkaz:
http://arxiv.org/abs/2303.14922
Occluded person re-identification (Re-ID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Many methods use the background as artificial occlusion and rely on attention networ
Externí odkaz:
http://arxiv.org/abs/2303.10976