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Found 35 papers, 8 papers with code
Learning to Evaluate Humor in Memes Based on the Incongruity Theory
Memes are a widely used means of communication on social media platforms, and are known for their ability to “go viral”.
Finding and Generating a Missing Part for Story Completion
We first conduct an experiment focusing on MPP, and our analysis shows that highly accurate predictions can be obtained when the missing part of a story is the beginning or the end.
Style-NeRF2NeRF: 3D Style Transfer From Style-Aligned Multi-View Images
Given a NeRF model reconstructed from a set of multi-view images, we perform 3D style transfer by refining the source NeRF model using stylized images generated by a style-aligned image-to-image diffusion model.
Stabilizing Extreme Q-learning by Maclaurin Expansion
However, issues remain, such as the instability caused by the exponential term in the loss function and the risk of the error distribution deviating from the Gumbel distribution.
HyperVQ: MLR-based Vector Quantization in Hyperbolic Space
The success of models operating on tokenized data has heightened the need for effective tokenization methods, particularly in vision and auditory tasks where inputs are naturally continuous.
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential.
Fully Spiking Denoising Diffusion Implicit Models
Spiking neural networks (SNNs) have garnered considerable attention owing to their ability to run on neuromorphic devices with super-high speeds and remarkable energy efficiencies.
HiPerformer: Hierarchically Permutation-Equivariant Transformer for Time Series Forecasting
It is imperative to discern the relationships between multiple time series for accurate forecasting.
Domain Adaptive Multiple Instance Learning for Instance-level Prediction of Pathological Images
We conducted experiments on the pathological image dataset we created for this study and showed that the proposed method significantly improves the classification performance compared to existing methods.
Self-Supervised Learning for Group Equivariant Neural Networks
To ensure that training is consistent with the equivariance, we propose two concepts for self-supervised tasks: equivariant pretext labels and invariant contrastive loss.
Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment
Firstly, it lets the domain classifier focus on features that are important for the classification, and, secondly, it couples the classification and adversarial branch more closely.
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Grouped self-attention mechanism for a memory-efficient Transformer
Time-series data analysis is important because numerous real-world tasks such as forecasting weather, electricity consumption, and stock market involve predicting data that vary over time.
Computational Storytelling and Emotions: A Survey
Storytelling has always been vital for human nature.
Learning from Label Proportions with Instance-wise Consistency
Learning from Label Proportions (LLP) is a weakly supervised learning method that aims to perform instance classification from training data consisting of pairs of bags containing multiple instances and the class label proportions within the bags.
ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer
Image narrative generation is a task to create a story from an image with a subjective viewpoint.
Unsupervised Pose-Aware Part Decomposition for 3D Articulated Objects
Articulated objects exist widely in the real world.
A Theoretical and Empirical Model of the Generalization Error under Time-Varying Learning Rate
In this study, we analyze the generalization bound for the time-varying case by applying PAC-Bayes and experimentally show that the theoretical functional form for the batch size and learning rate approximates the generalization error well for both cases.
Fully Spiking Variational Autoencoder
Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed and ultra-low energy consumption because of their binary and event-driven nature.
Video Moment Retrieval with Text Query Considering Many-to-Many Correspondence Using Potentially Relevant Pair
In this paper, we propose a novel training method that takes advantage of potentially relevant pairs, which are detected based on linguistic analysis about text annotation.
Efficient training for future video generation based on hierarchical disentangled representation of latent variables
In this paper, we propose a novel method for generating future prediction videos with less memory usage than the conventional methods.
Neural Star Domain as Primitive Representation
We show that NSD is a universal approximator of the star domain and is not only parsimonious and semantic but also an implicit and explicit shape representation.
Hyperbolic Neural Networks++
Hyperbolic spaces, which have the capacity to embed tree structures without distortion owing to their exponential volume growth, have recently been applied to machine learning to better capture the hierarchical nature of data.
Multi-Stage Pathological Image Classification using Semantic Segmentation
If we consider the relationship of neighboring patches and global features, we can improve the classification performance.
Toward a Better Story End: Collecting Human Evaluation with Reasons
In this study, we undertake the task of story ending generation.
Rethinking Task and Metrics of Instance Segmentation on 3D Point Clouds
Certain existing studies have split input point clouds into small regions such as 1m x 1m; one reason for this is that models in the studies cannot consume a large number of points because of the large space complexity.
Compact Approximation for Polynomial of Covariance Feature
Subsequently, we apply the proposed approximation to the polynomial corresponding to the matrix square root to obtain a compact approximation for the square root of the covariance feature.
Scalable Generative Models for Graphs with Graph Attention Mechanism
Graphs are ubiquitous real-world data structures, and generative models that approximate distributions over graphs and derive new samples from them have significant importance.
Invariant Feature Coding using Tensor Product Representation
Based on this result, a novel feature model that explicitly consider group action is proposed for principal component analysis and k-means clustering, which are commonly used in most feature coding methods, and global feature functions.
Long-Term Human Video Generation of Multiple Futures Using Poses
First, from an input human video, we generate sequences of future human poses (i. e., the image coordinates of their body-joints) via adversarial learning.
End-to-End Learning Using Cycle Consistency for Image-to-Caption Transformations
So far, research to generate captions from images has been carried out from the viewpoint that a caption holds sufficient information for an image.
Weakly supervised collective feature learning from curated media
Through these insights, we can define human curated groups as weak labels from which our proposed framework can learn discriminative features as a representation in the space of semantic concepts the users intended when creating the groups.
DeMIAN: Deep Modality Invariant Adversarial Network
To obtain the common representations under such a situation, we propose to make the distributions over different modalities similar in the learned representations, namely modality-invariant representations.
Kernel Approximation via Empirical Orthogonal Decomposition for Unsupervised Feature Learning
Our experiments show that the proposed method is better than the random features method and comparable with the Nystrom method in terms of the approximation error and classification accuracy.
Common Subspace for Model and Similarity: Phrase Learning for Caption Generation From Images
In order to overcome the shortage of training samples, CoSMoS obtains a subspace in which (a) all feature vectors associated with the same phrase are mapped as mutually close, (b) classifiers for each phrase are learned, and (c) training samples are shared among co-occurring phrases.
