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Found 26 papers, 6 papers with code
N-Modal Contrastive Losses with Applications to Social Media Data in Trimodal Space
Recent advancements in model architectures such as CLIP have enabled researchers to begin studying the interplay between the modalities of text and images in a shared latent space.
Pixel-Grounded Prototypical Part Networks
Prototypical part neural networks (ProtoPartNNs), namely PROTOPNET and its derivatives, are an intrinsically interpretable approach to machine learning.
NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios
With the increasing reliance on small Unmanned Aerial Systems (sUAS) for Emergency Response Scenarios, such as Search and Rescue, the integration of computer vision capabilities has become a key factor in mission success.
C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap
This is something rarely seen on social media, where the vast majority of text content is ``commentative'' in nature.
Has the Virtualization of the Face Changed Facial Perception? A Study of the Impact of Augmented Reality on Facial Perception
Our results demonstrate that faces modified with more traditional face filters are perceived similarly to unmodified faces, and faces filtered with augmented reality filters are perceived differently from unmodified faces.
Measuring Human Perception to Improve Open Set Recognition
The human ability to recognize when an object belongs or does not belong to a particular vision task outperforms all open set recognition algorithms.
Analyzing the Impact of Shape & Context on the Face Recognition Performance of Deep Networks
In this article, we analyze how changing the underlying 3D shape of the base identity in face images can distort their overall appearance, especially from the perspective of deep face recognition.
Motif Mining: Finding and Summarizing Remixed Image Content
On the internet, images are no longer static; they have become dynamic content.
Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems
Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as collision avoidance, search and rescue, and object manipulation.
The Criminality From Face Illusion
A few recent publications have claimed success in analyzing an image of a person's face in order to predict the person's status as Criminal / Non-Criminal.
Automatic Discovery of Political Meme Genres with Diverse Appearances
This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres.
Learning Transformation-Aware Embeddings for Image Forensics
To establish the efficacy of the proposed approach, comparisons with state-of-the-art handcrafted and deep learning-based descriptors, and image matching approaches are made.
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation
Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks.
Learning-Free Iris Segmentation Revisited: A First Step Toward Fast Volumetric Operation Over Video Samples
Subject matching performance in iris biometrics is contingent upon fast, high-quality iris segmentation.
Face Hallucination Revisited: An Exploratory Study on Dataset Bias
This down-sampling (or degradation) procedure not only defines the characteristics of the LR training data, but also determines the type of image degradations the learned FH models are eventually able to handle.
Backdooring Convolutional Neural Networks via Targeted Weight Perturbations
Deep learning techniques are at the top of the game for facial recognition, which means they have now been implemented in many production-level systems.
The Limits and Potentials of Deep Learning for Robotics
In this paper we discuss a number of robotics-specific learning, reasoning, and embodiment challenges for deep learning.
Robotics
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
We first demonstrate that our framework achieves double the throughput of a standard distributed hyperparameter optimization framework by optimizing SVM for MNIST using 150 distributed workers.
Provenance Filtering for Multimedia Phylogeny
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time.
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media.
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows.
Using Human Brain Activity to Guide Machine Learning
The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.
Predicting First Impressions with Deep Learning
Psychologists believe that these judgements are based on a variety of factors such as emotional states, personality traits, and other physiognomic cues.
To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?
If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?
One-Class Slab Support Vector Machine
This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM.
