18 citations · 73 across the 28 of their papers we have counts for
21 papers · 1 filter
The Fourth Monocular Depth Estimation Challenge
Anton Obukhov, Matteo Poggi, Fabio Tosi +54
This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a…
The Third Monocular Depth Estimation Challenge
Jaime Spencer, Fabio Tosi, Matteo Poggi +38
This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-…
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV
Jaime Spencer, Chris Russell, Simon Hadfield +1
Self-supervised learning is the key to unlocking generic computer vision systems. By eliminating the reliance on ground-truth annotations, it allows scaling to much larger data qua…
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
Jaime Spencer, Chris Russell, Simon Hadfield +1
Self-supervised monocular depth estimation (SS-MDE) has the potential to scale to vast quantities of data. Unfortunately, existing approaches limit themselves to the automotive dom…
The Second Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Michaela Trescakova +40
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, includin…
CERiL: Continuous Event-based Reinforcement Learning
Celyn Walters, Simon Hadfield
This paper explores the potential of event cameras to enable continuous time reinforcement learning. We formalise this problem where a continuous stream of unsynchronised observati…
The Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Chris Russell +16
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular…
SVS: Adversarial refinement for sparse novel view synthesis
Violeta Menéndez González, Andrew Gilbert, Graeme Phillipson +2
This paper proposes Sparse View Synthesis. This is a view synthesis problem where the number of reference views is limited, and the baseline between target and reference view is si…
EDeNN: Event Decay Neural Networks for low latency vision
Celyn Walters, Simon Hadfield
Despite the success of neural networks in computer vision tasks, digital 'neurons' are a very loose approximation of biological neurons. Today's learning approaches are designed to…
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
Jaime Spencer, Chris Russell, Simon Hadfield +1
This paper presents an open and comprehensive framework to systematically evaluate state-of-the-art contributions to self-supervised monocular depth estimation. This includes pretr…
Adaptive sampling for scanning pixel cameras
Yusuf Duman, Jean-Yves Guillemaut, Simon Hadfield
A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited. It produces data as a sequence of samples extracted from various parts of the scene d…
SaiNet: Stereo aware inpainting behind objects with generative networks
Violeta Menéndez González, Andrew Gilbert, Graeme Phillipson +2
In this work, we present an end-to-end network for stereo-consistent image inpainting with the objective of inpainting large missing regions behind objects. The proposed model cons…
SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition
Nikolina Kubiak, Armin Mustafa, Graeme Phillipson +2
We present SILT, a Self-supervised Implicit Lighting Transfer method. Unlike previous research on scene relighting, we do not seek to apply arbitrary new lighting configurations to…
TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression
Nikolina Kubiak, Simon Hadfield
We present TACTIC: Task-Aware Compression Through Intelligent Coding. Our lossy compression model learns based on the rate-distortion-accuracy trade-off for a specific task. By con…
EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance
Celyn Walters, Simon Hadfield
The broad scope of obstacle avoidance has led to many kinds of computer vision-based approaches. Despite its popularity, it is not a solved problem. Traditional computer vision tec…
Multi-channel Transformers for Multi-articulatory Sign Language Translation
Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore. In this pa…
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
Necati Cihan Camgoz, Oscar Koller, Simon Hadfield +1
Prior work on Sign Language Translation has shown that having a mid-level sign gloss representation (effectively recognizing the individual signs) improves the translation performa…
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
Jaime Spencer, Richard Bowden, Simon Hadfield
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency. Such approaches lack…
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
Jaime Spencer, Richard Bowden, Simon Hadfield
"Like night and day" is a commonly used expression to imply that two things are completely different. Unfortunately, this tends to be the case for current visual feature representa…
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
Jaime Spencer, Richard Bowden, Simon Hadfield
How do computers and intelligent agents view the world around them? Feature extraction and representation constitutes one the basic building blocks towards answering this question.…
Localisation via Deep Imagination: learn the features not the map
Jaime Spencer, Oscar Mendez, Richard Bowden +1
How many times does a human have to drive through the same area to become familiar with it? To begin with, we might first build a mental model of our surroundings. Upon revisiting…