Zobrazeno 1 - 10
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pro vyhledávání: '"Shaharabany, Tal"'
The task of audio captioning is similar in essence to tasks such as image and video captioning. However, it has received much less attention. We propose three desiderata for captioning audio -- (i) fluency of the generated text, (ii) faithfulness of
Externí odkaz:
http://arxiv.org/abs/2309.03884
It has been established that training a box-based detector network can enhance the localization performance of weakly supervised and unsupervised methods. Moreover, we extend this understanding by demonstrating that these detectors can be utilized to
Externí odkaz:
http://arxiv.org/abs/2309.03874
A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion m
Externí odkaz:
http://arxiv.org/abs/2306.09004
The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. However, it fails to reproduce such results for Out-Of-Distribution (OOD)
Externí odkaz:
http://arxiv.org/abs/2306.06370
Given an input image, and nothing else, our method returns the bounding boxes of objects in the image and phrases that describe the objects. This is achieved within an open world paradigm, in which the objects in the input image may not have been enc
Externí odkaz:
http://arxiv.org/abs/2206.09358
Autor:
Shaharabany, Tal, Wolf, Lior
The leading segmentation methods represent the output map as a pixel grid. We study an alternative representation in which the object edges are modeled, per image patch, as a polygon with $k$ vertices that is coupled with per-patch label probabilitie
Externí odkaz:
http://arxiv.org/abs/2112.02535
Autor:
Shaharabany, Tal, Wolf, Lior
In the weakly supervised localization setting, supervision is given as an image-level label. We propose to employ an image classifier $f$ and to train a generative network $g$ that outputs, given the input image, a per-pixel weight map that indicates
Externí odkaz:
http://arxiv.org/abs/2111.14131
Radiologist examination of chest CT is an effective way for screening COVID-19 cases. In this work, we overcome three challenges in the automation of this process: (i) the limited number of supervised positive cases, (ii) the lack of region-based sup
Externí odkaz:
http://arxiv.org/abs/2103.13677
We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architect
Externí odkaz:
http://arxiv.org/abs/1912.00367
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