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Found 22 papers, 7 papers with code
Multi-Modal Hallucination Control by Visual Information Grounding
In particular, we show that as more tokens are generated, the reliance on the visual prompt decreases, and this behavior strongly correlates with the emergence of hallucinations.
Meaning Representations from Trajectories in Autoregressive Models
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text.
Prompt Algebra for Task Composition
We investigate whether prompts learned independently for different tasks can be later combined through prompt algebra to obtain a model that supports composition of tasks.
Generate then Select: Open-ended Visual Question Answering Guided by World Knowledge
The open-ended Visual Question Answering (VQA) task requires AI models to jointly reason over visual and natural language inputs using world knowledge.
Benchmarking Diverse-Modal Entity Linking with Generative Models
Entities can be expressed in diverse formats, such as texts, images, or column names and cell values in tables.
Train/Test-Time Adaptation with Retrieval
Second, we apply ${\rm T^3AR}$ for test-time adaptation and show that exploiting a pool of external images at test-time leads to more robust representations over existing methods on DomainNet-126 and VISDA-C, especially when few adaptation data are available (up to 8%).
Linear Spaces of Meanings: Compositional Structures in Vision-Language Models
These vectors can be seen as "ideal words" for generating concepts directly within the embedding space of the model.
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call "\`a-la-carte learning".
A-La-Carte Prompt Tuning (APT): Combining Distinct Data via Composable Prompting
During inference, models can be assembled based on arbitrary selections of data sources, which we call a-la-carte learning.
Open-set Adversarial Defense with Clean-Adversarial Mutual Learning
This paper proposes an Open-Set Defense Network with Clean-Adversarial Mutual Learning (OSDN-CAML) as a solution to the OSAD problem.
Federated Generalized Face Presentation Attack Detection
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition
First, we learn generative features using the one-class data with a generative framework.
One-Class Classification: A Survey
One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class.
Open-set Adversarial Defense
In this paper, we show that open-set recognition systems are vulnerable to adversarial attacks.
Anomaly Detection-Based Unknown Face Presentation Attack Detection
Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users.
Quickest Intruder Detection for Multiple User Active Authentication
In this paper, we investigate how to detect intruders with low latency for Active Authentication (AA) systems with multiple-users.
Generative-Discriminative Feature Representations for Open-Set Recognition
We address the problem of open-set recognition, where the goal is to determine if a given sample belongs to one of the classes used for training a model (known classes).
Federated Face Presentation Attack Detection
A face presentation attack detection model with good generalization can be obtained when it is trained with face images from different input distributions and different types of spoof attacks.
OCGAN: One-class Novelty Detection Using GANs with Constrained Latent Representations
The key contribution of our work is our proposal to explicitly constrain the latent space to exclusively represent the given class.
Ranked #6 on
Anomaly Detection
on Hyper-Kvasir Dataset
Deep Transfer Learning for Multiple Class Novelty Detection
We show that thresholding the maximal activation of the proposed network can be used to identify novel objects effectively.
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks
In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images.
Generative Adversarial Network
Multimodal Unsupervised Image-To-Image Translation
+2
