Zobrazeno 1 - 10
of 283
pro vyhledávání: '"Gao, Junyu"'
Multimodal semantic segmentation shows significant potential for enhancing segmentation accuracy in complex scenes. However, current methods often incorporate specialized feature fusion modules tailored to specific modalities, thereby restricting inp
Externí odkaz:
http://arxiv.org/abs/2408.01343
From paired image-text training to text-only training for image captioning, the pursuit of relaxing the requirements for high-cost and large-scale annotation of good quality data remains consistent. In this paper, we propose Text-only Synthesis for I
Externí odkaz:
http://arxiv.org/abs/2405.18258
Multimodal semantic segmentation is a pivotal component of computer vision and typically surpasses unimodal methods by utilizing rich information set from various sources.Current models frequently adopt modality-specific frameworks that inherently bi
Externí odkaz:
http://arxiv.org/abs/2405.15365
Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous efforts are dedicated to mining segmentation-guiding visual cues from
Externí odkaz:
http://arxiv.org/abs/2405.13686
Few-shot learning aims to generalize the recognizer from seen categories to an entirely novel scenario. With only a few support samples, several advanced methods initially introduce class names as prior knowledge for identifying novel classes. Howeve
Externí odkaz:
http://arxiv.org/abs/2405.12543
Crowd localization targets on predicting each instance precise location within an image. Current advanced methods propose the pixel-wise binary classification to tackle the congested prediction, in which the pixel-level thresholds binarize the predic
Externí odkaz:
http://arxiv.org/abs/2404.13992
Object counting is a hot topic in computer vision, which aims to estimate the number of objects in a given image. However, most methods only count objects of a single category for an image, which cannot be applied to scenes that need to count objects
Externí odkaz:
http://arxiv.org/abs/2401.10530
With the emergence of foundation model, this novel paradigm of deep learning has encouraged many powerful achievements in natural language processing and computer vision. There are many advantages of foundation model, such as excellent feature extrac
Externí odkaz:
http://arxiv.org/abs/2401.06374
The ability to accurately comprehend natural language instructions and navigate to the target location is essential for an embodied agent. Such agents are typically required to execute user instructions in an online manner, leading us to explore the
Externí odkaz:
http://arxiv.org/abs/2311.13209
Autor:
Sun, Shengkai, Liu, Daizong, Dong, Jianfeng, Qu, Xiaoye, Gao, Junyu, Yang, Xun, Wang, Xun, Wang, Meng
Unsupervised pre-training has shown great success in skeleton-based action understanding recently. Existing works typically train separate modality-specific models, then integrate the multi-modal information for action understanding by a late-fusion
Externí odkaz:
http://arxiv.org/abs/2311.03106