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Found 36 papers, 0 papers with code
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning
This paper introduces a novel Parameter-Efficient Fine-Tuning (PEFT) framework for multi-modal, multi-task transfer learning with pre-trained language models.
Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals
Experimental results show that the proposed approach outperforms the benchmark method, with the weighted fusion method and guided attention modules both contributing to enhanced performance.
Magnifying Networks for Images with Billions of Pixels
However, deep neural networks are trained on images with resolutions that rarely exceed $1, 000 \times 1, 000$ pixels.
Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition
Being spontaneous, micro-expressions are useful in the inference of a person's true emotions even if an attempt is made to conceal them.
Micro Expression Recognition
Micro-Expression Recognition
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Sequential Normalization: an improvement over Ghost Normalization
Batch normalization (BatchNorm) is an effective yet poorly understood technique for neural network optimization.
A New Look at Ghost Normalization
Batch normalization (BatchNorm) is an effective yet poorly understood technique for neural network optimization.
Understanding Ancient Coin Images
In recent years, a range of problems within the broad umbrella of automatic, computer vision based analysis of ancient coins has been attracting an increasing amount of attention.
Colorectal Cancer Outcome Prediction from H&E Whole Slide Images using Machine Learning and Automatically Inferred Phenotype Profiles
Digital pathology (DP) is a new research area which falls under the broad umbrella of health informatics.
Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone
Access to low cost retinal imaging devices in low and middle income countries is limited, compromising progress in preventing needless blindness.
Achieving stable subspace clustering by post-processing generic clustering results
We propose an effective subspace selection scheme as a post-processing step to improve results obtained by sparse subspace clustering (SSC).
Identification of promising research directions using machine learning aided medical literature analysis
The rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions.
Highly accurate gaze estimation using a consumer RGB-depth sensor
The method we propose starts by performing accurate head pose estimation achieved by fitting a person specific morphable model of the face to depth data.
Descriptor transition tables for object retrieval using unconstrained cluttered video acquired using a consumer level handheld mobile device
In this paper we propose a novel recognition method from video sequences suitable for retrieval from databases acquired in highly unconstrained conditions e. g. using a mobile consumer-level device such as a phone.
Learnt quasi-transitive similarity for retrieval from large collections of faces
We are interested in identity-based retrieval of face sets from large unlabelled collections acquired in uncontrolled environments.
Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?
Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning.
Discovering topic structures of a temporally evolving document corpus
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.
Automatic vehicle tracking and recognition from aerial image sequences
This paper addresses the problem of automated vehicle tracking and recognition from aerial image sequences.
The adaptable buffer algorithm for high quantile estimation in non-stationary data streams
The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications.
Viewpoint distortion compensation in practical surveillance systems
Our aim is to estimate the perspective-effected geometric distortion of a scene from a video feed.
Groupwise registration of aerial images
This paper addresses the task of time separated aerial image registration.
Automatic Face Recognition from Video
The objective of this work is to automatically recognize faces from video sequences in a realistic, unconstrained setup in which illumination conditions are extreme and greatly changing, viewpoint and user motion pattern have a wide variability, and video input is of low quality.
Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension.
Sample-targeted clinical trial adaptation
Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial.
A unified framework for thermal face recognition
The reduction of the cost of infrared (IR) cameras in recent years has made IR imaging a highly viable modality for face recognition in practice.
A framework for improving the performance of verification algorithms with a low false positive rate requirement and limited training data
Our approach is based on the interpretation of an inter-pattern distance as implicitly defining a pattern embedding which approximately distributes patterns according to an isotropic multi-variate normal distribution in some space.
Hallucinating optimal high-dimensional subspaces
Linear subspace representations of appearance variation are pervasive in computer vision.
Infrared face recognition: a comprehensive review of methodologies and databases
Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications.
Computer simulation based parameter selection for resistance exercise
In contrast to most scientific disciplines, sports science research has been characterized by comparatively little effort investment in the development of relevant phenomenological models.
Matching objects across the textured-smooth continuum
However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein.
Live-wire 3D medical images segmentation
This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation.
Discriminative extended canonical correlation analysis for pattern set matching
Our second major contribution is to show that in contrast to CCA, E-CCA is readily adapted to match sets in a discriminative learning scheme which we call discriminative extended canonical correlation analysis (DE-CCA).
Vesselness features and the inverse compositional AAM for robust face recognition using thermal IR
To overcome the problem of thermal IR image sensitivity to the pattern of facial temperature emissions we describe a representation based on reliable anatomical features.
Infrared face recognition: a literature review
Automatic face recognition (AFR) is an area with immense practical potential which includes a wide range of commercial and law enforcement applications, and it continues to be one of the most active research areas of computer vision.
Illumination-invariant face recognition from a single image across extreme pose using a dual dimension AAM ensemble in the thermal infrared spectrum
Over the course of the last decade, infrared (IR) and particularly thermal IR imaging based face recognition has emerged as a promising complement to conventional, visible spectrum based approaches which continue to struggle when applied in practice.
Assessing Blinding in Clinical Trials
The current standard is follow up the trial with an auxiliary questionnaire, which allows trial participants to express their belief concerning the assigned intervention and which is used to compute a measure of the extent of blinding in the trial.
