Zobrazeno 1 - 10
of 65
pro vyhledávání: '"Kacem, Anis"'
Autor:
Karadeniz, Ahmet Serdar, Mallis, Dimitrios, Mejri, Nesryne, Cherenkova, Kseniya, Kacem, Anis, Aouada, Djamila
We propose PICASSO, a novel framework CAD sketch parameterization from hand-drawn or precise sketch images via rendering self-supervision. Given a drawing of a CAD sketch, the proposed framework turns it into parametric primitives that can be importe
Externí odkaz:
http://arxiv.org/abs/2407.13394
Autor:
Dupont, Elona, Cherenkova, Kseniya, Mallis, Dimitrios, Gusev, Gleb, Kacem, Anis, Aouada, Djamila
3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper proposes TransCAD, an end-to-end transformer-based architecture that
Externí odkaz:
http://arxiv.org/abs/2407.12702
Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, these techniques demand substantial computational resources, primarily d
Externí odkaz:
http://arxiv.org/abs/2404.12403
Autor:
Khan, Mohammad Sadil, Dupont, Elona, Ali, Sk Aziz, Cherenkova, Kseniya, Kacem, Anis, Aouada, Djamila
Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process behind a physical object given its 3D scan. We propose CAD-SIGNet, an
Externí odkaz:
http://arxiv.org/abs/2402.17678
Autor:
Nguyen, Dat, Mejri, Nesryne, Singh, Inder Pal, Kuleshova, Polina, Astrid, Marcella, Kacem, Anis, Ghorbel, Enjie, Aouada, Djamila
This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection are mainly based on a supervised binary classifier coupled with an
Externí odkaz:
http://arxiv.org/abs/2401.13856
Autor:
Sinha, Nilotpal, Shabayek, Abd El Rahman, Kacem, Anis, Rostami, Peyman, Shneider, Carl, Aouada, Djamila
Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to automatically design the architecture of a neural network for a specific task and target hardware. However, evaluating the performance of candidate architectures is a key chall
Externí odkaz:
http://arxiv.org/abs/2311.03923
Autor:
Mallis, Dimitrios, Ali, Sk Aziz, Dupont, Elona, Cherenkova, Kseniya, Karadeniz, Ahmet Serdar, Khan, Mohammad Sadil, Kacem, Anis, Gusev, Gleb, Aouada, Djamila
Recent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processes and relating them to real objects. In this context, 3D reverse engin
Externí odkaz:
http://arxiv.org/abs/2308.15966
Autor:
Shneider, Carl, Rostami, Peyman, Kacem, Anis, Sinha, Nilotpal, Shabayek, Abd El Rahman, Aouada, Djamila
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. D
Externí odkaz:
http://arxiv.org/abs/2307.09994
Autor:
Sinha, Ashish, Kawahara, Jeremy, Pakzad, Arezou, Abhishek, Kumar, Ruthven, Matthieu, Ghorbel, Enjie, Kacem, Anis, Aouada, Djamila, Hamarneh, Ghassan
In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis. However, existing datasets in this domain have significant limitations, including a small number of image samples, limited disease conditions
Externí odkaz:
http://arxiv.org/abs/2305.12621
Autor:
Cherenkova, Kseniya, Dupont, Elona, Kacem, Anis, Arzhannikov, Ilya, Gusev, Gleb, Aouada, Djamila
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas. Though the quality of 3D scans depends on the technical characteristics of the 3D scanner, the common drawback is the smoothing of
Externí odkaz:
http://arxiv.org/abs/2304.06531