3 citations · 7 across the 16 of their papers we have counts for
21 papers
Extremely Fast and Compact Binary Graph Representations via Randomized Operator Sketching
Srajan Agarwal, Megha P, Bikas C Das +2
Graph neural networks typically rely on dense, floating-point node representations, which can impose substantial memory and computational costs. Binary graph hashing offers an alte…
PuzzleMate: Benchmarking MLLMs for Egocentric Puzzle Assistance
Avijit Dasgupta, Shayon Dasgupta, Zakaria Laskar +2
Personal AI assistants hold the potential to evolve from digital interfaces into embodied companions capable of guiding users through complex physical activities. For these assista…
MV2: Multi-View Multi-Vehicle Driving Dataset for Novel View Synthesis
Sanjay Bhargav Dharavath, Hanvitha Saraswathi Mukkamala, Faizan Farooq Khan +4
Differentiable rendering has advanced novel view synthesis (NVS), yet applying it to real-world driving remains difficult due to sparse capture viewpoints, dynamic objects, and lim…
Real-Time Human Reconstruction and Animation using Feed-Forward Gaussian Splatting
Devdoot Chatterjee, Zakaria Laskar, C. V. Jawahar
We present HumanGS, a generalizable feed-forward Gaussian splatting framework for human 3D reconstruction and real-time animation from sparse multi-view RGB images and their associ…
PhyEduVideo: A Benchmark for Evaluating Text-to-Video Models for Physics Education
Megha Mariam K. M, Aditya Arun, Zakaria Laskar +1
Generative AI models, particularly Text-to-Video (T2V) systems, offer a promising avenue for transforming science education by automating the creation of engaging and intuitive vis…
Instance-Level Generation for Representation Learning
Yankun Wu, Zakaria Laskar, Giorgos Kordopatis-Zilos +2
Instance-level recognition (ILR) focuses on identifying individual objects rather than broad categories, offering the highest granularity in image classification. However, this fin…
Category-level Text-to-Image Retrieval Improved: Bridging the Domain Gap with Diffusion Models and Vision Encoders
Faizan Farooq Khan, Vladan Stojnić, Zakaria Laskar +2
This work explores text-to-image retrieval for queries that specify or describe a semantic category. While vision-and-language models (VLMs) like CLIP offer a straightforward open-…
A Dataset for Semantic Segmentation in the Presence of Unknowns
Zakaria Laskar, Tomas Vojir, Matej Grcic +6
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalie…
ILIAS: Instance-Level Image retrieval At Scale
Giorgos Kordopatis-Zilos, Vladan Stojnić, Anna Manko +7
This work introduces ILIAS, a new test dataset for Instance-Level Image retrieval At Scale. It is designed to evaluate the ability of current and future foundation models and retri…
Composed Image Retrieval for Training-Free Domain Conversion
Nikos Efthymiadis, Bill Psomas, Zakaria Laskar +4
This work addresses composed image retrieval in the context of domain conversion, where the content of a query image is retrieved in the domain specified by the query text. We show…
Differentiable Product Quantization for Memory Efficient Camera Relocalization
Zakaria Laskar, Iaroslav Melekhov, Assia Benbihi +2
Camera relocalization relies on 3D models of the scene with a large memory footprint that is incompatible with the memory budget of several applications. One solution to reduce the…
Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning
Vladan Stojnić, Zakaria Laskar, Giorgos Tolias
Deep active learning in the presence of outlier examples poses a realistic yet challenging scenario. Acquiring unlabeled data for annotation requires a delicate balance between avo…
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov +4
Visual localization is critical to many applications in computer vision and robotics. To address single-image RGB localization, state-of-the-art feature-based methods match local d…
Digging Into Self-Supervised Learning of Feature Descriptors
Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li +2
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks. However, most of them require per-pixel…
Continual Learning for Image-Based Camera Localization
Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov +2
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component. Directly regressing camera pose/3D scene…
Data-Efficient Ranking Distillation for Image Retrieval
Zakaria Laskar, Juho Kannala
Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. Re…
Geometric Image Correspondence Verification by Dense Pixel Matching
Zakaria Laskar, Iaroslav Melekhov, Hamed R. Tavakoli +2
This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The…
Semantic Matching by Weakly Supervised 2D Point Set Registration
Zakaria Laskar, Hamed R. Tavakoli, Juho Kannala
In this paper we address the problem of establishing correspondences between different instances of the same object. The problem is posed as finding the geometric transformation th…
Semi-Supervised Semantic Matching
Zakaria Laskar, Juho Kannala
Convolutional neural networks (CNNs) have been successfully applied to solve the problem of correspondence estimation between semantically related images. Due to non-availability o…
Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network
Zakaria Laskar, Iaroslav Melekhov, Surya Kalia +1
We propose a new deep learning based approach for camera relocalization. Our approach localizes a given query image by using a convolutional neural network (CNN) for first retrievi…
Context Aware Query Image Representation for Particular Object Retrieval
Zakaria Laskar, Juho Kannala
The current models of image representation based on Convolutional Neural Networks (CNN) have shown tremendous performance in image retrieval. Such models are inspired by the inform…