Researcher:
Kesen, İlker

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İlker

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Kesen

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Kesen, İlker

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2018 - 201912020 - 20223

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Researcher Of Publications: search.filters.isAuthorOfPublication.a97303db-3c79-403f-a8d5-9752bb164f73

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    Modulating bottom-up and top-down visual processing via language-conditional filters
    (Ieee, 2022) Erdem, Erkut; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Kesen, İlker; Can, Ozan Arkan; Erdem, Aykut; Yüret, Deniz; PhD Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; 20331; 179996
    How to best integrate linguistic and perceptual processing in multi-modal tasks that involve language and vision is an important open problem. In this work, we argue that the common practice of using language in a top-down manner, to direct visual attention over high-level visual features, may not be optimal. We hypothesize that the use of language to also condition the bottom-up processing from pixels to high-level features can provide benefits to the overall performance. To support our claim, we propose a U-Net-based model and perform experiments on two language-vision dense-prediction tasks: referring expression segmentation and language-guided image colorization. We compare results where either one or both of the top-down and bottom-up visual branches are conditioned on language. Our experiments reveal that using language to control the filters for bottom-up visual processing in addition to top-down attention leads to better results on both tasks and achieves competitive performance. Our linguistic analysis suggests that bottom-up conditioning improves segmentation of objects especially when input text refers to low-level visual concepts. Code is available at https://github.com/ilkerkesen/bvpr.
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    CRAFT: a benchmark for causal reasoning about forces and inTeractions
    (Assoc Computational Linguistics-Acl, 2022) Ates, Tayfun; Atesoglu, M. Samil; Yigit, Cagatay; N/A; N/A; Department of Computer Engineering; Department of Psychology; Department of Computer Engineering; Kesen, İlker; Kobaş, Mert; Erdem, Aykut; Göksun, Tilbe; Yüret, Deniz; PhD Student; Master Student; Faculty Member; Faculty Member; Faculty Member; Department of Psychology; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Social Sciences and Humanities; College of Engineering; College of Social Sciences and Humanities; College of Engineering; N/A; N/A; 20331; 47278; 179996
    Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.
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    Fast multidimensional reduction and broadcast operations on GPU for machine learning
    (Wiley, 2018) N/A; N/A; N/A; Department of Computer Engineering; Department of Computer Engineering; Dikbayır, Doğa; Çoban, Enis Berk; Kesen, İlker; Yüret, Deniz; Erten, Didem Unat; Master Student; Master Student; PhD Student; Faculty Member; Faculty Member; Department of Computer Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; N/A; N/A; 179996; 219274
    Reduction and broadcast operations are commonly used in machine learning algorithms for different purposes. They widely appear in the calculation of the gradient values of a loss function, which are one of the core structures of neural networks. Both operations are implemented naively in many libraries usually for scalar reduction or broadcast; however, to our knowledge, there are no optimized multidimensional implementations available. This fact limits the performance of machine learning models requiring these operations to be performed on tensors. In this work, we address the problem and propose two new strategies that extend the existing implementations to perform on tensors. We introduce formal definitions of both operations using tensor notations, investigate their mathematical properties, and exploit these properties to provide an efficient solution for each. We implement our parallel strategies and test them on a CUDA enabled Tesla K40m GPU accelerator. Our performant implementations achieve up to 75% of the peak device memory bandwidth on different tensor sizes and dimensions. Significant speedups against the implementations available in the Knet Deep Learning framework are also achieved for both operations.
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    PublicationOpen Access
    Craft: a benchmark for causal reasoning about forces and in teractions
    (Association for Computational Linguistics (ACL), 2022) Ateş, Tayfun; Ateşoğlu, M. Şamil; Yiğit, Çağatay; Department of Computer Engineering; Department of Psychology; Erdem, Aykut; Göksun, Tilbe; Yüret, Deniz; Kesen, İlker; Kobaş, Mert; Faculty Member; Faculty Member; Faculty Member; Master Student; Department of Computer Engineering; Department of Psychology; Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI); Graduate School of Sciences and Engineering; College of Engineering; College of Social Sciences and Humanities; 20331; 47278; 179996; N/A; N/A; N/A
    Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark.