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Found 20 papers, 6 papers with code
The Bad Batches: Enhancing Self-Supervised Learning in Image Classification Through Representative Batch Curation
The pursuit of learning robust representations without human supervision is a longstanding challenge.
The role of noise in denoising models for anomaly detection in medical images
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
A deep mixture density network for outlier-corrected interpolation of crowd-sourced weather data
However, as the number of available observation sites increases, so too does the opportunity for data quality issues to emerge, particularly given that many of these sensors do not have the benefit of official maintenance teams.
Transformer-Encoder Detector Module: Using Context to Improve Robustness to Adversarial Attacks on Object Detection
Deep neural network approaches have demonstrated high performance in object recognition (CNN) and detection (Faster-RCNN) tasks, but experiments have shown that such architectures are vulnerable to adversarial attacks (FFF, UAP): low amplitude perturbations, barely perceptible by the human eye, can lead to a drastic reduction in labeling performance.
Bayesian deep learning for mapping via auxiliary information: a new era for geostatistics?
Here we demonstrate the power of feature learning in a geostatistical context, by showing how deep neural networks can automatically learn the complex relationships between point-sampled target variables and gridded auxiliary variables (such as those provided by remote sensing), and in doing so produce detailed maps of chosen target variables.
Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
Human emotions analysis has been the focus of many studies, especially in the field of Affective Computing, and is important for many applications, e. g. human-computer intelligent interaction, stress analysis, interactive games, animations, etc.
A framework for probabilistic weather forecast post-processing across models and lead times using machine learning
In this paper, we use a road surface temperature example to demonstrate a three-stage framework that uses machine learning to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support: probabilities of future weather outcomes.
Image Captioning through Image Transformer
Inspired by the successes in text analysis and translation, previous work have proposed the \textit{transformer} architecture for image captioning.
Contextual Relabelling of Detected Objects
Contextual information, such as the co-occurrence of objects and the spatial and relative size among objects provides deep and complex information about scenes.
Human Attention in Image Captioning: Dataset and Analysis
In this work, we present a novel dataset consisting of eye movements and verbal descriptions recorded synchronously over images.
Understanding and Visualizing Deep Visual Saliency Models
Our analyses reveal that: 1) some visual regions (e. g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e. g. head) over some others (e. g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e. g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.
On-Policy Trust Region Policy Optimisation with Replay Buffers
Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies.
What Catches the Eye? Visualizing and Understanding Deep Saliency Models
Deep convolutional neural networks have demonstrated high performances for fixation prediction in recent years.
Aggregated Sparse Attention for Steering Angle Prediction
In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction.
Salient Region Segmentation
Early saliency models were based on low-level hand-crafted feature derived from insights gained in neuroscience and psychophysics.
Deep saliency: What is learnt by a deep network about saliency?
Moreover we argue that this transformation leads to the emergence of receptive fields conceptually similar to the centre-surround filters hypothesized by early research on visual saliency.
Combination of Supervised and Reinforcement Learning For Vision-Based Autonomous Control
In this article we propose a model-free control method, which uses a combination of reinforcement and supervised learning for autonomous control and paves the way towards policy based control in real world environments.
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.
Sign Spotting using Hierarchical Sequential Patterns with Temporal Intervals
This paper tackles the problem of spotting a set of signs occuring in videos with sequences of signs.
