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Found 35 papers, 8 papers with code
International AI Safety Report
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems.
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models
Our approach, named EAGLE, is fully agnostic to the LLM or fusion module and works as a post-pretraining approach that improves the grounding and language alignment of the visual encoder.
International Scientific Report on the Safety of Advanced AI (Interim Report)
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI.
Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models
This paper introduces a Multi-modal Evaluation Benchmark named MERLIM, a scalable test-bed to assess the capabilities of IT-LVLMs on fundamental computer vision tasks.
Studying Generalization on Memory-Based Methods in Continual Learning
One of the objectives of Continual Learning is to learn new concepts continually over a stream of experiences and at the same time avoid catastrophic forgetting.
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information
Existing question answering methods often assume that the input content (e. g., documents or videos) is always accessible to solve the task.
PIVOT: Prompting for Video Continual Learning
In this paper, we address the problem of continual learning for video data.
How Relevant is Selective Memory Population in Lifelong Language Learning?
Lifelong language learning seeks to have models continuously learn multiple tasks in a sequential order without suffering from catastrophic forgetting.
A Study on the Predictability of Sample Learning Consistency
Curriculum Learning is a powerful training method that allows for faster and better training in some settings.
Memory Population in Continual Learning via Outlier Elimination
This paper introduces Memory Outlier Elimination (MOE), a method for identifying and eliminating outliers in the memory buffer by choosing samples from label-homogeneous subpopulations.
Entropy-based Stability-Plasticity for Lifelong Learning
The ability to continuously learn remains elusive for deep learning models.
Evaluation Benchmarks for Spanish Sentence Representations
Due to the success of pre-trained language models, versions of languages other than English have been released in recent years.
Augmenting BERT-style Models with Predictive Coding to Improve Discourse-level Representations
Current language models are usually trained using a self-supervised scheme, where the main focus is learning representations at the word or sentence level.
DeepSocNav: Social Navigation by Imitating Human Behaviors
Current datasets to train social behaviors are usually borrowed from surveillance applications that capture visual data from a bird's-eye perspective.
TNT: Text-Conditioned Network with Transductive Inference for Few-Shot Video Classification
Recently, few-shot learning has received increasing interest.
Optimizing Reusable Knowledge for Continual Learning via Metalearning
On the other hand, a set of trainable masks provides the key mechanism to selectively choose from the KB relevant weights to solve each task.
Inspecting the concept knowledge graph encoded by modern language models
The field of natural language understanding has experienced exponential progress in the last few years, with impressive results in several tasks.
Temporal Attention Modules for Memory-Augmented Neural Networks
We introduce two temporal attention modules which can be plugged into traditional memory augmented recurrent neural networks to improve their performance in natural language processing tasks.
Tracking the progress of Language Models by extracting their underlying Knowledge Graphs
The state of the art, previously dominated by pre-trained word embeddings, is now being pushed forward by large pre-trained contextual representation models.
DACT-BERT: Increasing the efficiency and interpretability of BERT by using adaptive computation time.
DACT-BERT adds an adaptive computation mechanism to the regular processing pipeline of BERT.
Catching the Long Tail in Deep Neural Networks
As a working hypothesis, we speculate that during learning some weights focus on mining patterns from frequent examples while others are in charge of memorizing rare long-tail samples.
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods.
Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism
We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation.
Differentiable Adaptive Computation Time for Visual Reasoning
This paper presents a novel attention-based algorithm for achieving adaptive computation called DACT, which, unlike existing ones, is end-to-end differentiable.
A Behavioral Approach to Visual Navigation with Graph Localization Networks
Inspired by research in psychology, we introduce a behavioral approach for visual navigation using topological maps.
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation
We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation.
End-to-End Joint Semantic Segmentation of Actors and Actions in Video
Traditional video understanding tasks include human action recognition and actor/object semantic segmentation.
Interpretable Visual Question Answering by Visual Grounding from Attention Supervision Mining
A key aspect of VQA models that are interpretable is their ability to ground their answers to relevant regions in the image.
A Deep Learning Based Behavioral Approach to Indoor Autonomous Navigation
We present a semantically rich graph representation for indoor robotic navigation.
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Advances in image processing and computer vision in the latest years have brought about the use of visual features in artwork recommendation.
Exploring Content-based Artwork Recommendation with Metadata and Visual Features
Compared to other areas, artwork recommendation has received little attention, despite the continuous growth of the artwork market.
How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval
Consequently, a main conclusion of this work is that general-purpose commonsense ontologies improve performance on visual reasoning tasks when properly filtered to select meaningful visual relations.
A Hierarchical Pose-Based Approach to Complex Action Understanding Using Dictionaries of Actionlets and Motion Poselets
In this paper, we introduce a new hierarchical model for human action recognition using body joint locations.
Action Recognition in Video Using Sparse Coding and Relative Features
In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection.
Discriminative Hierarchical Modeling of Spatio-Temporally Composable Human Activities
Our method describes human activities in a hierarchical discriminative model that operates at three semantic levels.
