no code implementations • gwll (LREC) 2022 • Christian Chiarcos, Katerina Gkirtzou, Maxim Ionov, Besim Kabashi, Fahad Khan, Ciprian-Octavian Truică
Following presentations of frequency and attestations, and embeddings and distributional similarity, this paper introduces the third cornerstone of the emerging OntoLex module for Frequency, Attestation and Corpus-based Information, OntoLex-FrAC.
no code implementations • LT4HALA (LREC) 2022 • Valeria Quochi, Andrea Bellandi, Fahad Khan, Michele Mallia, Francesca Murano, Silvia Piccini, Luca Rigobianco, Alessandro Tommasi, Cesare Zavattari
Available language technology is hardly applicable to scarcely attested ancient languages, yet their digital semantic representation, though challenging, is an asset for the purpose of sharing and preserving existing cultural knowledge.
no code implementations • LDL (ACL) 2022 • Christian Chiarcos, Katerina Gkirtzou, Fahad Khan, Penny Labropoulou, Marco Passarotti, Matteo Pellegrini
This paper describes the current status of the emerging OntoLex module for linguistic morphology.
no code implementations • LDL (ACL) 2022 • Fahad Khan, Christian Chiarcos, Thierry Declerck, Maria Pia di Buono, Milan Dojchinovski, Jorge Gracia, Giedre Valunaite Oleskeviciene, Daniela Gifu
This article discusses a survey carried out within the NexusLinguarum COST Action which aimed to give an overview of existing guidelines (GLs) and best practices (BPs) in linguistic linked data.
no code implementations • LREC 2022 • Fahad Khan, Francisco J. Minaya Gómez, Rafael Cruz González, Harry Diakoff, Javier E. Diaz Vera, John P. McCrae, Ciara O’Loughlin, William Michael Short, Sander Stolk
In this paper we will discuss our preliminary work towards the construction of a WordNet for Old English, taking our inspiration from other similar WN construction projects for ancient languages such as Ancient Greek, Latin and Sanskrit.
1 code implementation • 27 Feb 2024 • Hanan Gani, Muzammal Naseer, Fahad Khan, Salman Khan
The proposed approach induces contextual knowledge in the network by learning to reconstruct the missing organ or parts of an organ in the output segmentation space.
1 code implementation • 2 Jan 2024 • Dmitry Demidov, Roba Al Majzoub, Amandeep Kumar, Fahad Khan
Multi-class colorectal tissue classification is a challenging problem that is typically addressed in a setting, where it is assumed that ample amounts of training data is available.
1 code implementation • 25 Nov 2023 • Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, Junwei Han
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks.
1 code implementation • 22 Nov 2023 • Shehan Munasinghe, Rusiru Thushara, Muhammad Maaz, Hanoona Abdul Rasheed, Salman Khan, Mubarak Shah, Fahad Khan
Extending image-based Large Multimodal Models (LMMs) to videos is challenging due to the inherent complexity of video data.
1 code implementation • 9 Oct 2023 • Yang Bai, Xinxing Xu, Yong liu, Salman Khan, Fahad Khan, WangMeng Zuo, Rick Siow Mong Goh, Chun-Mei Feng
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption.
Ranked #1 on Image Retrieval on CIRR
1 code implementation • NeurIPS 2023 • Mohamed El Amine Boudjoghra, Salwa K. Al Khatib, Jean Lahoud, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan, Fahad Khan
We argue that such a closed-world assumption is restrictive and explore for the first time 3D indoor instance segmentation in an open-world setting, where the model is allowed to distinguish a set of known classes as well as identify an unknown object as unknown and then later incrementally learning the semantic category of the unknown when the corresponding category labels are available.
1 code implementation • 24 Aug 2023 • Sheng Zhang, Muzammal Naseer, Guangyi Chen, Zhiqiang Shen, Salman Khan, Kun Zhang, Fahad Khan
To address this challenge, we propose the Self Structural Semantic Alignment (S^3A) framework, which extracts the structural semantic information from unlabeled data while simultaneously self-learning.
no code implementations • 21 Mar 2023 • Zhiqiang Dong, Jiale Cao, Rao Muhammad Anwer, Jin Xie, Fahad Khan, Yanwei Pang
Given a set of sparse and learnable proposals, LEAPS employs a dynamic person search head to directly perform person detection and corresponding re-id feature generation without non-maximum suppression post-processing.
no code implementations • 23 Feb 2023 • Muzammal Naseer, Ahmad Mahmood, Salman Khan, Fahad Khan
Our temporal prompts are the result of a learnable transformation that allows optimizing for temporal gradients during an adversarial attack to fool the motion dynamics.
1 code implementation • CVPR 2023 • Sheng Zhang, Salman Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, Fahad Khan
The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes.
1 code implementation • 6 Oct 2022 • Vishal Thengane, Salman Khan, Munawar Hayat, Fahad Khan
In this work, we show that a frozen CLIP (Contrastive Language-Image Pretraining) model offers astounding continual learning performance without any fine-tuning (zero-shot evaluation).
1 code implementation • 25 Jul 2022 • Abdelrahman Mohamed, Rushali Grandhe, K J Joseph, Salman Khan, Fahad Khan
In contrast to a recent ViT based CIL approach, our $\textrm{D}^3\textrm{Former}$ does not dynamically expand its architecture when new tasks are learned and remains suitable for a large number of incremental tasks.
no code implementations • 20 Jul 2022 • Fatima Albreiki, Sultan Abughazal, Jean Lahoud, Rao Anwer, Hisham Cholakkal, Fahad Khan
To the best of our knowledge, we are the first to investigate the robustness of point-based 3D object detectors.
no code implementations • LREC 2020 • Fahad Khan
The increasing recognition of the utility of Linked Data as a means of publishing lexical resource has helped to underline the need for RDF based data models which have the flexibility and expressivity to be able to represent the most salient kinds of information contained in such resources as structured data, including, notably, information relating to time and the temporal dimension.
1 code implementation • LREC 2020 • Sina Ahmadi, John Philip McCrae, Sanni Nimb, Fahad Khan, Monica Monachini, Bolette Pedersen, Thierry Declerck, Tanja Wissik, Bell, Andrea i, Irene Pisani, Thomas Troelsg{\aa}rd, Sussi Olsen, Simon Krek, Veronika Lipp, Tam{\'a}s V{\'a}radi, L{\'a}szl{\'o} Simon, Andr{\'a}s Gyorffy, Carole Tiberius, Tanneke Schoonheim, Yifat Ben Moshe, Maya Rudich, Raya Abu Ahmad, Dorielle Lonke, Kira Kovalenko, Margit Langemets, Jelena Kallas, Oksana Dereza, Theodorus Fransen, David Cillessen, David Lindemann, Mikel Alonso, Ana Salgado, Jos{\'e} Luis Sancho, Rafael-J. Ure{\~n}a-Ruiz, Jordi Porta Zamorano, Kiril Simov, Petya Osenova, Zara Kancheva, Ivaylo Radev, Ranka Stankovi{\'c}, Andrej Perdih, Dejan Gabrovsek
Aligning senses across resources and languages is a challenging task with beneficial applications in the field of natural language processing and electronic lexicography.
no code implementations • LREC 2020 • Fahad Khan, Laurent Romary, Ana Salgado, Jack Bowers, Mohamed Khemakhem, Toma Tasovac
In this article we will introduce two of the new parts of the new multi-part version of the Lexical Markup Framework (LMF) ISO standard, namely part 3 of the standard (ISO 24613-3), which deals with etymological and diachronic data, and Part 4 (ISO 24613-4), which consists of a TEI serialisation of all of the prior parts of the model.
no code implementations • 23 May 2019 • Laurent Romary, Mohamed Khemakhem, Fahad Khan, Jack Bowers, Nicoletta Calzolari, Monte George, Mandy Pet, Piotr Bański
Lexical Markup Framework (LMF) or ISO 24613 [1] is a de jure standard that provides a framework for modelling and encoding lexical information in retrodigitised print dictionaries and NLP lexical databases.
no code implementations • WS 2016 • Fahad Khan, Bell, Andrea i, Monica Monachini
This article describes work on enabling the addition of temporal information to senses of words in linguistic linked open data lexica based on the lemonDia model.
no code implementations • LREC 2016 • Ouafae Nahli, Francesca Frontini, Monica Monachini, Fahad Khan, Arsalan Zarghili, Mustapha Khalfi
This paper describes the conversion into LMF, a standard lexicographic digital format of {`}al-q{\=a}m{\=u}s al-muḥ{\=\i}ṭ, a Medieval Arabic lexicon.
no code implementations • LREC 2016 • Riccardo Del Gratta, Francesca Frontini, Monica Monachini, Gabriella Pardelli, Irene Russo, Roberto Bartolini, Fahad Khan, Claudia Soria, Nicoletta Calzolari
This proposal describes a new way to visualise resources in the LREMap, a community-built repository of language resource descriptions and uses.
no code implementations • LREC 2014 • Massimo Moneglia, Susan Brown, Francesca Frontini, Gloria Gagliardi, Fahad Khan, Monica Monachini, Aless Panunzi, ro
IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages.