Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Shin, Gyungin"'
Autor:
Li, Dylan, Shin, Gyungin
Unsupervised instance segmentation aims to segment distinct object instances in an image without relying on human-labeled data. This field has recently seen significant advancements, partly due to the strong local correspondences afforded by rich vis
Externí odkaz:
http://arxiv.org/abs/2409.18961
Enabling engagement of manga by visually impaired individuals presents a significant challenge due to its inherently visual nature. With the goal of fostering accessibility, this paper aims to generate a dialogue transcript of a complete manga chapte
Externí odkaz:
http://arxiv.org/abs/2408.00298
Without accurate transcription of numerical data in scientific documents, a scientist cannot draw accurate conclusions. Unfortunately, the process of copying numerical data from one paper to another is prone to human error. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2306.07968
Segmentation is a core computer vision competency, with applications spanning a broad range of scientifically and economically valuable domains. To date, however, the prohibitive cost of annotation has limited the deployment of flexible segmentation
Externí odkaz:
http://arxiv.org/abs/2304.14376
The goal of this work is to segment and name regions of images without access to pixel-level labels during training. To tackle this task, we construct segmenters by distilling the complementary strengths of two foundation models. The first, CLIP (Rad
Externí odkaz:
http://arxiv.org/abs/2209.11228
Semantic segmentation has a broad range of applications, but its real-world impact has been significantly limited by the prohibitive annotation costs necessary to enable deployment. Segmentation methods that forgo supervision can side-step these cost
Externí odkaz:
http://arxiv.org/abs/2206.07045
In this paper, we tackle the challenging task of unsupervised salient object detection (SOD) by leveraging spectral clustering on self-supervised features. We make the following contributions: (i) We revisit spectral clustering and demonstrate its po
Externí odkaz:
http://arxiv.org/abs/2203.12614
A central challenge for the task of semantic segmentation is the prohibitive cost of obtaining dense pixel-level annotations to supervise model training. In this work, we show that in order to achieve a good level of segmentation performance, all you
Externí odkaz:
http://arxiv.org/abs/2104.06394
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