Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Chakraborty, Souradeep"'
Autor:
Chakraborty, Souradeep, Wei, Zijun, Kelton, Conor, Ahn, Seoyoung, Balasubramanian, Aruna, Zelinsky, Gregory J., Samaras, Dimitris
Publikováno v:
IEEE Transactions on Multimedia 25 (2022): 4478-4493
We present a model for predicting visual attention during the free viewing of graphic design documents. While existing works on this topic have aimed at predicting static saliency of graphic designs, our work is the first attempt to predict both spat
Externí odkaz:
http://arxiv.org/abs/2407.02439
Autor:
Chakraborty, Souradeep, Perez, Dana, Friedman, Paul, Sheuka, Natallia, Friedman, Constantin, Yaskiv, Oksana, Gupta, Rajarsi, Zelinsky, Gregory J., Saltz, Joel H., Samaras, Dimitris
We present a method for classifying the expertise of a pathologist based on how they allocated their attention during a cancer reading. We engage this decoding task by developing a novel method for predicting the attention of pathologists as they rea
Externí odkaz:
http://arxiv.org/abs/2403.17255
Our paper introduces a novel two-stage self-supervised approach for detecting co-occurring salient objects (CoSOD) in image groups without requiring segmentation annotations. Unlike existing unsupervised methods that rely solely on patch-level inform
Externí odkaz:
http://arxiv.org/abs/2403.11107
In this paper, we address the detection of co-occurring salient objects (CoSOD) in an image group using frequency statistics in an unsupervised manner, which further enable us to develop a semi-supervised method. While previous works have mostly focu
Externí odkaz:
http://arxiv.org/abs/2311.06654
Autor:
Chakraborty, Souradeep, Ma, Ke, Gupta, Rajarsi, Knudsen, Beatrice, Zelinsky, Gregory J., Saltz, Joel H., Samaras, Dimitris
We study the attention of pathologists as they examine whole-slide images (WSIs) of prostate cancer tissue using a digital microscope. To the best of our knowledge, our study is the first to report in detail how pathologists navigate WSIs of prostate
Externí odkaz:
http://arxiv.org/abs/2202.08437
Autor:
Chakraborty, Souradeep
In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market. In order to do this, we present a novel Markov decis
Externí odkaz:
http://arxiv.org/abs/1907.04373
Autor:
Chakraborty, Souradeep, Mitra, Pabitra
We present an algorithm for graph based saliency computation that utilizes the underlying dense subgraphs in finding visually salient regions in an image. To compute the salient regions, the model first obtains a saliency map using random walks on a
Externí odkaz:
http://arxiv.org/abs/1511.06545
Autor:
Chakraborty, Souradeep, Mitra, Pabitra
Publikováno v:
In Computer Vision and Image Understanding April 2016 145:1-14
Akademický článek
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Autor:
Chakraborty, Souradeep, Mitra, Pabitra
Publikováno v:
In Journal of Visual Communication and Image Representation November 2015 33:20-30