1 citations · 1 across the 8 of their papers we have counts for
11 papers
Decoder-Agnostic Token Merging for Vision Transformers: A Systematic Study of G2TM
Victor Bercy, Martyna Poreba, Michal Szczepanski +1
Vision Transformers (ViTs) have achieved state-of-the-art performance across a range of computer vision tasks, mainly thanks to the self-attention mechanism. However, its complexit…
DynEoMT: Learning Object Dynamicity from Online Segmentation Queries
Calvin Galagain, Martyna Poreba, François Goulette
Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the observing camera. This dynamicity…
Enabling Fully Integer-Only Inference for Lightweight Detection Transformers
Thanh Cong Le, Michal Szczepanski, Martyna Poreba
Vision Transformer detectors now approach the accuracy of CNNs but remain difficult to deploy on NPUs and microcontrollers because key components, including deformable attention, f…
It Takes Few to TANGO: A Quantized Distributed Model for Binaural Speech Enhancement
Zahra Benslimane, Pierre Chouteau, Martyna Poreba +4
Neural network-based multichannel speech enhancement systems achieve strong enhancement performance, but their computational and memory requirements limit deployment on resource-co…
RT-Tango: Real-Time Distributed Binaural Speech Enhancement for Low-Power Hearing Aid Devices
Z. Benslimane, P. Chouteau, M. Poreba +4
Real-time binaural speech enhancement is constrained by latency, computational cost, and inter-device communication, yet existing efficient solutions predominantly address single-c…
Token-Space Mask Prediction for Efficient Vision Transformer Segmentation
Calvin Galagain, Martyna Poreba, François Goulette
Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from convolutional architectures. W…
LiPS: Lightweight Panoptic Segmentation for Resource-Constrained Robotics
Calvin Galagain, Martyna Poreba, François Goulette +1
Panoptic segmentation is a key enabler for robotic perception, as it unifies semantic understanding with object-level reasoning. However, the increasing complexity of state-of-the-…
Beyond Attention Scores: SVD-Based Vision Token Pruning for Efficient Vision-Language Models
Yvon Apedo, Martyna Poreba, Michal Szczepanski +1
Vision-Language Models (VLMs) have revolutionized multi-modal learning by jointly processing visual and textual information. Yet, they face significant challenges due to the high c…
Where Do Tokens Go? Understanding Pruning Behaviors in STEP at High Resolutions
Michal Szczepanski, Martyna Poreba, Karim Haroun
Vision Transformers (ViTs) achieve state-of-the-art performance in semantic segmentation but are hindered by high computational and memory costs. To address this, we propose STEP (…
I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation
Jordan Sassoon, Michal Szczepanski, Martyna Poreba
Vision Transformers (ViTs) have recently achieved strong results in semantic segmentation, yet their deployment on resource-constrained devices remains limited due to their high me…
Is Semantic SLAM Ready for Embedded Systems ? A Comparative Survey
Calvin Galagain, Martyna Poreba, François Goulette
Semantic SLAM holds promise for robust robot navigation in complex environments, but its practicality on embedded systems remains uncertain. This paper presents a comparative surve…