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Found 37 papers, 14 papers with code
S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network.
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTV
Self-supervised learning is the key to unlocking generic computer vision systems.
BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation
Cross-view image matching for geo-localisation is a challenging problem due to the significant visual difference between aerial and ground-level viewpoints.
ZeST-NeRF: Using temporal aggregation for Zero-Shot Temporal NeRFs
Recent approaches have had great success at performing novel view image synthesis of static scenes.
Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTV
Unfortunately, existing approaches limit themselves to the automotive domain, resulting in models incapable of generalizing to complex environments such as natural or indoor settings.
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
CERiL: Continuous Event-based Reinforcement Learning
We present a method to train on event streams derived from standard RL environments, thereby solving the proposed continuous time RL problem.
The Monocular Depth Estimation Challenge
This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
SVS: Adversarial refinement for sparse novel 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 significant.
EDeNN: Event Decay Neural Networks for low latency vision
Despite the success of neural networks in computer vision tasks, digital 'neurons' are a very loose approximation of biological neurons.
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Matter
It is likely that many papers were not only optimized for particular datasets, but also for errors in the data and evaluation criteria.
Adaptive sampling for scanning pixel cameras
A scanning pixel camera is a novel low-cost, low-power sensor that is not diffraction limited.
Generalizing to New Tasks via One-Shot Compositional Subgoals
In addition to improving learning efficiency for standard long-term tasks, this approach also makes it possible to perform one-shot generalization to previously unseen tasks, given only a single reference trajectory for the task in a different environment.
SaiNet: Stereo aware inpainting behind objects with generative networks
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.
SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning
In both cases we out-performed the SOTA by 30% in task success rate.
Medusa: Universal Feature Learning via Attentional Multitasking
We argue that MTL is a stepping stone towards universal feature learning (UFL), which is the ability to learn generic features that can be applied to new tasks without retraining.
SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition
We then remap this unified input domain using a discriminator that is presented with the generated outputs and the style reference, i. e. images of the desired illumination conditions.
TACTIC: Joint Rate-Distortion-Accuracy Optimisation for Low Bitrate Compression
We present TACTIC: Task-Aware Compression Through Intelligent Coding.
EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance
The broad scope of obstacle avoidance has led to many kinds of computer vision-based approaches.
Robot in a China Shop: Using Reinforcement Learning for Location-Specific Navigation Behaviour
Robots need to be able to work in multiple different environments.
Markov Localisation using Heatmap Regression and Deep Convolutional Odometry
Recent advances in deep learning hardware allow large likelihood volumes to be stored directly on the GPU, along with the hardware necessary to efficiently perform GPU-bound 3D convolutions and this obviates many of the disadvantages of grid based methods.
What Did You Think Would Happen? Explaining Agent Behaviour Through Intended Outcomes
We present a novel form of explanation for Reinforcement Learning, based around the notion of intended outcome.
Diagonal Memory Optimisation for Machine Learning on Micro-controllers
Micro-controller targets are identified where it is only possible to deploy some models if diagonal memory optimisation is used.
Multi-channel Transformers for Multi-articulatory Sign Language Translation
Sign languages use multiple asynchronous information channels (articulators), not just the hands but also the face and body, which computational approaches often ignore.
DeFeat-Net: General Monocular Depth via Simultaneous Unsupervised Representation Learning
In the current monocular depth research, the dominant approach is to employ unsupervised training on large datasets, driven by warped photometric consistency.
Same Features, Different Day: Weakly Supervised Feature Learning for Seasonal Invariance
The aim of this paper is to provide a dense feature representation that can be used to perform localization, sparse matching or image retrieval, regardless of the current seasonal or temporal appearance.
Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation
We report state-of-the-art sign language recognition and translation results achieved by our Sign Language Transformers.
Scale-Adaptive Neural Dense Features: Learning via Hierarchical Context Aggregation
In all cases, we show how incorporating SAND features results in better or comparable results to the baseline, whilst requiring little to no additional training.
Localisation via Deep Imagination: learn the features not the map
In order to build the embedded map, we train a deep Siamese Fully Convolutional U-Net to perform dense feature extraction.
Neural Sign Language Translation
SLR seeks to recognize a sequence of continuous signs but neglects the underlying rich grammatical and linguistic structures of sign language that differ from spoken language.
SubUNets: End-To-End Hand Shape and Continuous Sign Language Recognition
We propose a novel deep learning approach to solve simultaneous alignment and recognition problems (referred to as "Sequence-to-sequence" learning).
Ranked #14 on
Sign Language Recognition
on RWTH-PHOENIX-Weather 2014
Taking the Scenic Route to 3D: Optimising Reconstruction From Moving Cameras
This approach is ill-suited for reconstruction applications, where learning about the environment is more valuable than speed of traversal.
SeDAR - Semantic Detection and Ranging: Humans can localise without LiDAR, can robots?
Similarly, we do not extrude the 2D geometry present in the floorplan into 3D and try to align it to the real-world.
Exploiting High Level Scene Cues in Stereo Reconstruction
We present a novel approach to 3D reconstruction which is inspired by the human visual system.
Exploring Causal Relationships in Visual Object Tracking
We show that the location predictions are robust to camera shake and sud- den motion, which is invaluable for any tracking algorithm and demonstrate this by applying causal prediction to two state-of-the-art trackers.
Hollywood 3D: Recognizing Actions in 3D Natural Scenes
In addition, two state of the art action recognition algorithms are extended to make use of the 3D data, and five new interest point detection strategies are also proposed, that extend to the 3D data.
