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Found 16 papers, 10 papers with code
Leveraging Representations from Intermediate Encoder-blocks for Synthetic Image Detection
The recently developed and publicly available synthetic image generation methods and services make it possible to create extremely realistic imagery on demand, raising great risks for the integrity and safety of online information.
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must be covered in more detail.
RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection
Online misinformation is often multimodal in nature, i. e., it is caused by misleading associations between texts and accompanying images.
FairBranch: Fairness Conflict Correction on Task-group Branches for Fair Multi-Task Learning
The generalization capacity of Multi-Task Learning (MTL) becomes limited when unrelated tasks negatively impact each other by updating shared parameters with conflicting gradients, resulting in negative transfer and a reduction in MTL accuracy compared to single-task learning (STL).
Towards Fair Face Verification: An In-depth Analysis of Demographic Biases
This paper presents an in-depth analysis, with a particular emphasis on the intersectionality of these demographic factors.
FLAC: Fairness-Aware Representation Learning by Suppressing Attribute-Class Associations
To overcome these limitations, this work introduces FLAC, a methodology that minimizes mutual information between the features extracted by the model and a protected attribute, without the use of attribute labels.
Ranked #1 on
HairColor/Bias-conflicting
on CelebA
VERITE: A Robust Benchmark for Multimodal Misinformation Detection Accounting for Unimodal Bias
Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread.
MemeFier: Dual-stage Modality Fusion for Image Meme Classification
Hate speech is a societal problem that has significantly grown through the Internet.
Synthetic Misinformers: Generating and Combating Multimodal Misinformation
To address this, we perform a comparative study on existing and new Synthetic Misinformers that involves (1) out-of-context (OOC) image-caption pairs, (2) cross-modal named entity inconsistency (NEI) as well as (3) hybrid approaches and we evaluate them against real-world misinformation; using the COSMOS benchmark.
Design-time Fashion Popularity Forecasting in VR Environments
To this end, we develop a computer vision pipeline fine tuned on fashion imagery in order to extract relevant visual features along with the category and attributes of the garment.
Leveraging Large-scale Multimedia Datasets to Refine Content Moderation Models
The sheer volume of online user-generated content has rendered content moderation technologies essential in order to protect digital platform audiences from content that may cause anxiety, worry, or concern.
VICTOR: Visual Incompatibility Detection with Transformers and Fashion-specific contrastive pre-training
For fashion outfits to be considered aesthetically pleasing, the garments that constitute them need to be compatible in terms of visual aspects, such as style, category and color.
MemeTector: Enforcing deep focus for meme detection
To this end, we propose a methodology, called Visual Part Utilization, that utilizes the visual part of image memes as instances of the regular image class and the initial image memes as instances of the image meme class to force the model to concentrate on the critical parts that characterize an image meme.
InDistill: Information flow-preserving knowledge distillation for model compression
In this paper we introduce InDistill, a model compression approach that combines knowledge distillation and channel pruning in a unified framework for the transfer of the critical information flow paths from a heavyweight teacher to a lightweight student.
Multimodal Quasi-AutoRegression: Forecasting the visual popularity of new fashion products
We employ the product's target attributes time series as a proxy of temporal popularity patterns, mitigating the lack of historical data, while exogenous time series help capture trends among interrelated attributes.
Ranked #1 on
Popularity Forecasting
on SHIFT15M
LAVARNET: Neural Network Modeling of Causal Variable Relationships for Multivariate Time Series Forecasting
Multivariate time series forecasting is of great importance to many scientific disciplines and industrial sectors.
