19 citations · 59 across the 18 of their papers we have counts for
15 papers · 1 filter
Video Object Segmentation-Aware Audio Generation
Ilpo Viertola, Vladimir Iashin, Esa Rahtu
Existing multimodal audio generation models often lack precise user control, which limits their applicability in professional Foley workflows. In particular, these models focus on…
Temporally Aligned Audio for Video with Autoregression
Ilpo Viertola, Vladimir Iashin, Esa Rahtu
We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extra…
UDGS-SLAM : UniDepth Assisted Gaussian Splatting for Monocular SLAM
Mostafa Mansour, Ahmed Abdelsalam, Ari Happonen +2
Recent advancements in monocular neural depth estimation, particularly those achieved by the UniDepth network, have prompted the investigation of integrating UniDepth within a Gaus…
Synchformer: Efficient Synchronization from Sparse Cues
Vladimir Iashin, Weidi Xie, Esa Rahtu +1
Our objective is audio-visual synchronization with a focus on 'in-the-wild' videos, such as those on YouTube, where synchronization cues can be sparse. Our contributions include a…
FinnWoodlands Dataset
Juan Lagos, Urho Lempiö, Esa Rahtu
While the availability of large and diverse datasets has contributed to significant breakthroughs in autonomous driving and indoor applications, forestry applications are still lag…
PanDepth: Joint Panoptic Segmentation and Depth Completion
Juan Lagos, Esa Rahtu
Understanding 3D environments semantically is pivotal in autonomous driving applications where multiple computer vision tasks are involved. Multi-task models provide different type…
Supervised Fine-tuning Evaluation for Long-term Visual Place Recognition
Farid Alijani, Esa Rahtu
In this paper, we present a comprehensive study on the utility of deep convolutional neural networks with two state-of-the-art pooling layers which are placed after convolutional l…
Sparse in Space and Time: Audio-visual Synchronisation with Trainable Selectors
Vladimir Iashin, Weidi Xie, Esa Rahtu +1
The objective of this paper is audio-visual synchronisation of general videos 'in the wild'. For such videos, the events that may be harnessed for synchronisation cues may be spati…
SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion
Juan Pablo Lagos, Esa Rahtu
Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth c…
Beyond Visual Field of View: Perceiving 3D Environment with Echoes and Vision
Lingyu Zhu, Esa Rahtu, Hang Zhao
This paper focuses on perceiving and navigating 3D environments using echoes and RGB image. In particular, we perform depth estimation by fusing RGB image with echoes, received fro…
Taming Visually Guided Sound Generation
Vladimir Iashin, Esa Rahtu
Recent advances in visually-induced audio generation are based on sampling short, low-fidelity, and one-class sounds. Moreover, sampling 1 second of audio from the state-of-the-art…
V-SlowFast Network for Efficient Visual Sound Separation
Lingyu Zhu, Esa Rahtu
The objective of this paper is to perform visual sound separation: i) we study visual sound separation on spectrograms of different temporal resolutions; ii) we propose a new light…
Lightweight Monocular Depth with a Novel Neural Architecture Search Method
Lam Huynh, Phong Nguyen, Jiri Matas +2
This paper presents a novel neural architecture search method, called LiDNAS, for generating lightweight monocular depth estimation models. Unlike previous neural architecture sear…
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss
Lam Huynh, Matteo Pedone, Phong Nguyen +3
Deep neural networks have recently thrived on single image depth estimation. That being said, current developments on this topic highlight an apparent compromise between accuracy a…
On the Importance of Encrypting Deep Features
Xingyang Ni, Heikki Huttunen, Esa Rahtu
In this study, we analyze model inversion attacks with only two assumptions: feature vectors of user data are known, and a black-box API for inference is provided. On the one hand,…