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Found 12 papers, 8 papers with code
ManiFPT: Defining and Analyzing Fingerprints of Generative Models
Recent works have shown that generative models leave traces of their underlying generative process on the generated samples, broadly referred to as fingerprints of a generative model, and have studied their utility in detecting synthetic images from real ones.
Exploring Perceptual Limitation of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perception.
Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies
Deepfake videos present an increasing threat to society with potentially negative impact on criminal justice, democracy, and personal safety and privacy.
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation.
Towards Perceiving Small Visual Details in Zero-shot Visual Question Answering with Multimodal LLMs
In particular, we show that their zero-shot accuracy in answering visual questions is very sensitive to the size of the visual subject of the question, declining up to 46% with size.
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Ensuring a neural network is not relying on protected attributes (e. g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI.
Shadow Datasets, New challenging datasets for Causal Representation Learning
Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling.
Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions
Precision and Recall are two prominent metrics of generative performance, which were proposed to separately measure the fidelity and diversity of generative models.
Using Visual Cropping to Enhance Fine-Detail Question Answering of BLIP-Family Models
As our initial analysis of BLIP-family models revealed difficulty with answering fine-detail questions, we investigate the following question: Can visual cropping be employed to improve the performance of state-of-the-art visual question answering models on fine-detail questions?
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split.
Spatial Frequency Bias in Convolutional Generative Adversarial Networks
As the success of Generative Adversarial Networks (GANs) on natural images quickly propels them into various real-life applications across different domains, it becomes more and more important to clearly understand their limitations.
Disconnected Manifold Learning for Generative Adversarial Networks
Natural images may lie on a union of disjoint manifolds rather than one globally connected manifold, and this can cause several difficulties for the training of common Generative Adversarial Networks (GANs).
