1.2k citations · 1.6k across the 55 of their papers we have counts for
12 papers · 1 filter
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion
Lam Huynh, Phong Nguyen-Ha, Jiri Matas +2
In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance. Unlike existing depth completion methods, our approach performs well on extrem…
FACEGAN: Facial Attribute Controllable rEenactment GAN
Soumya Tripathy, Juho Kannala, Esa Rahtu
The face reenactment is a popular facial animation method where the person's identity is taken from the source image and the facial motion from the driving image. Recent works have…
RGBD-Net: Predicting color and depth images for novel views synthesis
Phong Nguyen-Ha, Animesh Karnewar, Lam Huynh +3
We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware g…
Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
Mayu Otani, Yuta Nakashima, Esa Rahtu +1
The query-based moment retrieval is a problem of localising a specific clip from an untrimmed video according a query sentence. This is a challenging task that requires interpretat…
Visually Guided Sound Source Separation using Cascaded Opponent Filter Network
Lingyu Zhu, Esa Rahtu
The objective of this paper is to recover the original component signals from a mixture audio with the aid of visual cues of the sound sources. Such task is usually referred as vis…
Learning to Learn to Compress
Nannan Zou, Honglei Zhang, Francesco Cricri +5
In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural networ…
Leveraging Category Information for Single-Frame Visual Sound Source Separation
Lingyu Zhu, Esa Rahtu
Visual sound source separation aims at identifying sound components from a given sound mixture with the presence of visual cues. Prior works have demonstrated impressive results, b…
A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
Vladimir Iashin, Esa Rahtu
Dense video captioning aims to localize and describe important events in untrimmed videos. Existing methods mainly tackle this task by exploiting only visual features, while comple…
End-to-End Learning for Video Frame Compression with Self-Attention
Nannan Zou, Honglei Zhang, Francesco Cricri +5
One of the core components of conventional (i.e., non-learned) video codecs consists of predicting a frame from a previously-decoded frame, by leveraging temporal correlations. In…
Sequential View Synthesis with Transformer
Phong Nguyen-Ha, Lam Huynh, Esa Rahtu +1
This paper addresses the problem of novel view synthesis by means of neural rendering, where we are interested in predicting the novel view at an arbitrary camera pose based on a g…
Guiding Monocular Depth Estimation Using Depth-Attention Volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas +2
Recovering the scene depth from a single image is an ill-posed problem that requires additional priors, often referred to as monocular depth cues, to disambiguate different 3D inte…
Multi-modal Dense Video Captioning
Vladimir Iashin, Esa Rahtu
Dense video captioning is a task of localizing interesting events from an untrimmed video and producing textual description (captions) for each localized event. Most of the previou…