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Found 44 papers, 18 papers with code
Multi-Source Open-Set Deep Adversarial Domain Adaptation
Besides, an adversarial learning strategy is followed to model the discriminator between the target-domain known and unknown classes.
Tafsir Dataset: A Novel Multi-Task Benchmark for Named Entity Recognition and Topic Modeling in Classical Arabic Literature
Various historical languages, which used to be lingua franca of science and arts, deserve the attention of current NLP research.
Cognitive Neural Architecture Search Reveals Hierarchical Entailment
Recent research has suggested that the brain is more shallow than previously thought, challenging the traditionally assumed hierarchical structure of the ventral visual pathway.
Human Gaze Boosts Object-Centered Representation Learning
To account for the importance of central vision in humans, we crop the visual area around the gaze location.
Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers
Shortcut learning, i. e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning algorithms, particularly when deploying them to assist in making sensitive decisions, such as in medical diagnostics.
The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies
To promote further collaboration between biological and artificial intelligence researchers, we introduce the 2025 edition of the Algonauts Project challenge: How the Human Brain Makes Sense of Multimodal Movies (https://algonautsproject. com/).
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process
We refer to the features learned with the Teacher's guidance as distilled features and the features irrelevant to the task and ignored by the Student as residual features.
Limited but consistent gains in adversarial robustness by co-training object recognition models with human EEG
Previous works relied on brain data acquired in rodents or primates using invasive techniques, from specific regions of the brain, under non-natural conditions (anesthetized animals), and with stimulus datasets lacking diversity and naturalness.
Human-inspired Explanations for Vision Transformers and Convolutional Neural Networks
We introduce Foveation-based Explanations (FovEx), a novel human-inspired visual explainability (XAI) method for Deep Neural Networks.
Classification of freshwater snails of the genus Radomaniola with multimodal triplet networks
In this paper, we present our first proposal of a machine learning system for the classification of freshwater snails of the genus Radomaniola.
Position: An Inner Interpretability Framework for AI Inspired by Lessons from Cognitive Neuroscience
Inner Interpretability is a promising emerging field tasked with uncovering the inner mechanisms of AI systems, though how to develop these mechanistic theories is still much debated.
Learning Object Semantic Similarity with Self-Supervision
To achieve this, the model exploits two distinct strategies: the visuo-language alignment ensures that different objects of the same category are represented similarly, whereas the temporal alignment leverages that objects from the same context are frequently seen in succession to make their representations more similar.
Generative Adversarial Collaborations: A practical guide for conference organizers and participating scientists
Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views.
Different Algorithms (Might) Uncover Different Patterns: A Brain-Age Prediction Case Study
Few of our models achieved state-of-the-art performance on the specific data-set we utilized.
Caregiver Talk Shapes Toddler Vision: A Computational Study of Dyadic Play
For this we propose a computational model of visual representation learning during dyadic play.
Learning Class and Domain Augmentations for Single-Source Open-Domain Generalization
Single-source open-domain generalization (SS-ODG) addresses the challenge of labeled source domains with supervision during training and unlabeled novel target domains during testing.
Analyzing Vision Transformers for Image Classification in Class Embedding Space
Inspired by previous research in NLP, we demonstrate how the inner representations at any level of the hierarchy can be projected onto the learned class embedding space to uncover how these networks build categorical representations for their predictions.
Net2Brain: A Toolbox to compare artificial vision models with human brain responses
We introduce Net2Brain, a graphical and command-line user interface toolbox for comparing the representational spaces of artificial deep neural networks (DNNs) and human brain recordings.
Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI
Therefore, a core challenge of the assessment process is to identify when experts from different disciplines talk about the same problem but use different terminologies.
What do navigation agents learn about their environment?
We use iSEE to probe the dynamic representations produced by these agents for the presence of information about the agent as well as the environment.
Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses
In this paper we present MusicVideos (MuVi), a novel dataset for affective multimedia content analysis to study how the auditory and visual modalities contribute to the perceived emotion of media.
FRIDA -- Generative Feature Replay for Incremental Domain Adaptation
Inspired by the notion of generative feature replay, we propose a novel framework called Feature Replay based Incremental Domain Adaptation (FRIDA) which leverages a new incremental generative adversarial network (GAN) called domain-generic auxiliary classification GAN (DGAC-GAN) for producing domain-specific feature representations seamlessly.
Generative Adversarial Network
Unsupervised Domain Adaptation
AttendAffectNet–Emotion Prediction of Movie Viewers Using Multimodal Fusion with Self-Attention
The models that use all visual, audio, and text features simultaneously as their inputs performed better than those using features extracted from each modality separately.
AttendAffectNet: Self-Attention based Networks for Predicting Affective Responses from Movies
In this work, we propose different variants of the self-attention based network for emotion prediction from movies, which we call AttendAffectNet.
Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning
In this paper, we tackle an open research question in transfer learning, which is selecting a model initialization to achieve high performance on a new task, given several pre-trained models.
Using Human Psychophysics to Evaluate Generalization in Scene Text Recognition Models
Relative to the attention-based (Attn) model, we discover that the connectionist temporal classification (CTC) model is more robust to noise and occlusion, and better at generalizing to different word lengths.
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching.
Predictive Coding Networks Meet Action Recognition
In this way, the model only relies on the video frames, and does not need pre-processed optical flows as input.
Latent space representation for multi-target speaker detection and identification with a sparse dataset using Triplet neural networks
When reducing the training data to only using the train set, our method results in 309 confusions for the Multi-target speaker identification task, which is 46% better than the baseline model.
Multimodal Deep Models for Predicting Affective Responses Evoked by Movies
Interestingly, we also observe that the optical flow is more informative than the RGB in videos, and overall, models using audio features are more accurate than those based on video features when making the final prediction of evoked emotions.
The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence
Recently, researchers of natural intelligence have begun using those AI models to explore how the brain performs such tasks.
Representation Similarity Analysis for Efficient Task taxonomy & Transfer Learning
We next evaluate the relationship of RSA with the transfer learning performance on Taskonomy tasks and a new task: Pascal VOC semantic segmentation.
Deep Anchored Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have been proven to be extremely successful at solving computer vision tasks.
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
Ranked #2 on
3D Instance Segmentation
on SceneNN
3D Instance Segmentation
3D Semantic Instance Segmentation
+3
Few-Shot Regression via Learned Basis Functions
We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection.
Do Deep Neural Networks Suffer from Crowding?
Also, for all tested networks, when trained on targets in isolation, we find that recognition accuracy of the networks decreases the closer the flankers are to the target and the more flankers there are.
Herding Generalizes Diverse M -Best Solutions
We show that the algorithm to extract diverse M -solutions from a Conditional Random Field (called divMbest [1]) takes exactly the form of a Herding procedure [2], i. e. a deterministic dynamical system that produces a sequence of hypotheses that respect a set of observed moment constraints.
Foveation-based Mechanisms Alleviate Adversarial Examples
To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly.
Self-Adaptable Templates for Feature Coding
Within this processing pipeline, the common trend is to learn the feature coding templates, often referred as codebook entries, filters, or over-complete basis.
Comment on "Ensemble Projection for Semi-supervised Image Classification"
In a series of papers by Dai and colleagues [1, 2], a feature map (or kernel) was introduced for semi- and unsupervised learning.
SEEDS: Superpixels Extracted via Energy-Driven Sampling
We define a robust and fast to evaluate energy function, based on enforcing color similarity between the bound- aries and the superpixel color histogram.
Random Binary Mappings for Kernel Learning and Efficient SVM
SVMs suffer from various drawbacks in terms of selecting the right kernel, which depends on the image descriptors, as well as computational and memory efficiency.
Sparse Quantization for Patch Description
We demonstrate the capabilities of our formulation for both keypoint matching and image classification.
