21 citations · 44 across the 11 of their papers we have counts for
13 papers
Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence
NVIDIA, :, Amala Sanjay Deshmukh +204
We introduce Nemotron 3 Nano Omni, the latest model in the Nemotron multimodal series and the first to natively support audio inputs alongside text, images, and video. Nemotron 3 N…
Stateful Token Reduction for Long-Video Hybrid VLMs
Jindong Jiang, Amala Sanjay Deshmukh, Kateryna Chumachenko +7
Token reduction accelerates long-video vision--language models (VLMs), but existing methods target Transformers, where reduction is treated as token pruning. We study token reducti…
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…
Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents
Ilia Karmanov, Amala Sanjay Deshmukh, Lukas Voegtle +8
Optical Character Recognition (OCR) technology is widely used to extract text from images of documents, facilitating efficient digitization and data retrieval. However, merely extr…
MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression Recognition in-the-wild
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
Dynamic Facial Expression Recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologi…
Improving Unimodal Inference with Multimodal Transformers
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
This paper proposes an approach for improving performance of unimodal models with multimodal training. Our approach involves a multi-branch architecture that incorporates unimodal…
Self-attention fusion for audiovisual emotion recognition with incomplete data
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this paper, we consider the problem of multimodal data analysis with a use case of audiovisual emotion recognition. We propose an architecture capable of learning from raw data…
Self-Attention Neural Bag-of-Features
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj
In this work, we propose several attention formulations for multivariate sequence data. We build on top of the recently introduced 2D-Attention and reformulate the attention learni…
Learning to ignore: rethinking attention in CNNs
Firas Laakom, Kateryna Chumachenko, Jenni Raitoharju +2
Recently, there has been an increasing interest in applying attention mechanisms in Convolutional Neural Networks (CNNs) to solve computer vision tasks. Most of these methods learn…
Ensembling object detectors for image and video data analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We fur…
Incremental Fast Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Moncef Gabbouj +1
This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kern…
Speed-up and multi-view extensions to Subclass Discriminant Analysis
Kateryna Chumachenko, Jenni Raitoharju, Alexandros Iosifidis +1
In this paper, we propose a speed-up approach for subclass discriminant analysis and formulate a novel efficient multi-view solution to it. The speed-up approach is developed based…
Machine Learning Based Analysis of Finnish World War II Photographers
Kateryna Chumachenko, Anssi Männistö, Alexandros Iosifidis +1
In this paper, we demonstrate the benefits of using state-of-the-art machine learning methods in the analysis of historical photo archives. Specifically, we analyze prominent Finni…