Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Itani, Hani"'
Autor:
Pérez, Juan C., Pardo, Alejandro, Soldan, Mattia, Itani, Hani, Leon-Alcazar, Juan, Ghanem, Bernard
This study investigates whether Compressed-Language Models (CLMs), i.e. language models operating on raw byte streams from Compressed File Formats~(CFFs), can understand files compressed by CFFs. We focus on the JPEG format as a representative CFF, g
Externí odkaz:
http://arxiv.org/abs/2405.17146
Autor:
Held, Jan, Itani, Hani, Cioppa, Anthony, Giancola, Silvio, Ghanem, Bernard, Van Droogenbroeck, Marc
The rapid advancement of artificial intelligence has led to significant improvements in automated decision-making. However, the increased performance of models often comes at the cost of explainability and transparency of their decision-making proces
Externí odkaz:
http://arxiv.org/abs/2404.06332
Autor:
Hammoud, Hasan Abed Al Kader, Itani, Hani, Pizzati, Fabio, Torr, Philip, Bibi, Adel, Ghanem, Bernard
We present SynthCLIP, a CLIP model trained on entirely synthetic text-image pairs. Leveraging recent text-to-image (TTI) networks and large language models (LLM), we generate synthetic datasets of images and corresponding captions at scale, with no h
Externí odkaz:
http://arxiv.org/abs/2402.01832
Autor:
Alfarra, Motasem, Itani, Hani, Pardo, Alejandro, Alhuwaider, Shyma, Ramazanova, Merey, Pérez, Juan C., Cai, Zhipeng, Müller, Matthias, Ghanem, Bernard
This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distributi
Externí odkaz:
http://arxiv.org/abs/2304.04795
The rapid advancement of chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explor
Externí odkaz:
http://arxiv.org/abs/2303.17760
Autor:
Itani, Hani
Large scale semantic segmentation is considered as one of the fundamental tasks in 3D scene understanding. Point clouds provide a basic and rich geometric representation of scenes and tangible objects. Convolutional Neural Networks (CNNs) have demons
Externí odkaz:
http://hdl.handle.net/10754/665898
In this work, we focus on designing a point local aggregation function that yields parameter efficient networks for 3D point cloud semantic segmentation. We explore the idea of using learnable neighbor-to-grid soft assignment in grid-based aggregatio
Externí odkaz:
http://arxiv.org/abs/2012.14929
Autor:
Alfarra, Motasem, Itani, Hani, Pardo, Alejandro, Alhuwaider, Shyma, Ramazanova, Merey, Pérez, Juan C., Cai, Zhipeng, Müller, Matthias, Ghanem, Bernard
This paper proposes a novel online evaluation protocol for Test Time Adaptation (TTA) methods, which penalizes slower methods by providing them with fewer samples for adaptation. TTA methods leverage unlabeled data at test time to adapt to distributi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f007c5daee4de6cd915a2e7dafe7b20
http://arxiv.org/abs/2304.04795
http://arxiv.org/abs/2304.04795
The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28b70a35418df33808acdd6c1db901e2
http://arxiv.org/abs/2303.17760
http://arxiv.org/abs/2303.17760