Search Results for author:
Found 23 papers, 8 papers with code
Gait Recognition from Highly Compressed Videos
We systematically evaluate the performance of our artifact correction model against a range of noisy surveillance data and demonstrate that our approach not only achieves improved pose estimation on low-quality surveillance footage, but also preserves the integrity of the pose estimation on high resolution footage.
Aligning Actions and Walking to LLM-Generated Textual Descriptions
For action recognition, we employ LLMs to generate textual descriptions of actions in the BABEL-60 dataset, facilitating the alignment of motion sequences with linguistic representations.
RoCode: A Dataset for Measuring Code Intelligence from Problem Definitions in Romanian
Recently, large language models (LLMs) have become increasingly powerful and have become capable of solving a plethora of tasks through proper instructions in natural language.
CrossGaze: A Strong Method for 3D Gaze Estimation in the Wild
Gaze estimation, the task of predicting where an individual is looking, is a critical task with direct applications in areas such as human-computer interaction and virtual reality.
The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models
Gait, an unobtrusive biometric, is valued for its capability to identify individuals at a distance, across external outfits and environmental conditions.
Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues
Depression, a prominent contributor to global disability, affects a substantial portion of the population.
GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning
Gait analysis is proven to be a reliable way to perform person identification without relying on subject cooperation.
Learning to Simplify Spatial-Temporal Graphs in Gait Analysis
However, these methods often rely on hand-crafted spatial-temporal graphs that are based on human anatomy disregarding the particularities of the dataset and task.
GaitPT: Skeletons Are All You Need For Gait Recognition
Our results show that GaitPT achieves state-of-the-art performance compared to other skeleton-based gait recognition works, in both controlled and in-the-wild scenarios.
PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait
In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns.
GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes
Gait, the manner of walking, has been proven to be a reliable biometric with uses in surveillance, marketing and security.
It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers
In this work, we propose a flexible time-enriched multimodal transformer architecture for detecting depression from social media posts, using pretrained models for extracting image and text embeddings.
An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder
This work proposes a transformer architecture for user-level classification of gambling addiction and depression that is trainable end-to-end.
Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression
In this work, we explore the relationship between depression and manifestations of happiness in social media.
BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer
Memes are prevalent on the internet and continue to grow and evolve alongside our culture.
Cultural Vocal Bursts Intensity Prediction
Meme Classification
+1
From Face to Gait: Weakly-Supervised Learning of Gender Information from Walking Patterns
Our results show on par or higher performance with facial analysis models with an F1 score of 91% and the ability to successfully generalise to scenarios in which facial analysis is unfeasible due to subjects not facing the camera or having the face obstructed.
Sequence-to-Sequence Lexical Normalization with Multilingual Transformers
Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.
Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT
Early risk detection of mental illnesses has a massive positive impact upon the well-being of people.
WildGait: Learning Gait Representations from Raw Surveillance Streams
Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple times in a straight line in front of a camera.
Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
To generate useful data samples for training the student, our framework (i) learns to generate images on a proxy data set (with images and classes different from those used to train the black-box) and (ii) applies an evolutionary strategy to make sure that each generated data sample exhibits a high response for a specific class when given as input to the black box.
A Generic and Model-Agnostic Exemplar Synthetization Framework for Explainable AI
In this work, we focus on explainable AI and propose a novel generic and model-agnostic framework for synthesizing input exemplars that maximize a desired response from a machine learning model.
Self-Supervised Representation Learning on Document Images
This work analyses the impact of self-supervised pre-training on document images in the context of document image classification.
CamLoc: Pedestrian Location Detection from Pose Estimation on Resource-constrained Smart-cameras
Video processing holds utility for many emerging applications and data labelling in the IoT space.
