Adaptive anchor methods improve multi-modal learning by balancing intra-modal and inter-modal information.
problem Fixed anchor methods limit multi-modal learning by over-reliance on a single modality and inadequate cross-modal correlation.
method Adaptive anchor methods using centroid-based anchors from all modalities.
result Adaptive anchor methods like CentroBind consistently outperform fixed anchor methods across various datasets.
KD-Net transfers knowledge from multi-modal to mono-modal segmentation networks.
problem Limited acquisition of multiple imaging modalities in clinical settings.
method Generalized distillation framework adapted for mono-modal networks.
result The student network outperforms baseline mono-modal networks in brain tumor segmentation.
MHVAE learns cross-modality inference inspired by human cognition.
problem Cross-modality inference in multimodal data.
method Hierarchical multimodal generative model with modality-specific and joint-modality distributions.
result MHVAE performs on par with state-of-the-art models on multimodal datasets.
The paper analyzes and proposes an algorithm for multi-modal nonlinear embeddings with theoretical performance bounds.
problem Generalizability of multi-modal nonlinear embeddings to unseen data.
method Theoretical analysis and a multi-modal nonlinear representation learning algorithm motivated by performance bounds.
result The proposed algorithm yields promising performance in multi-modal image classification and cross-modal image-text retrieval applications.
A novel deep learning technique combines multiple modalities, improving performance.
problem Challenges in leveraging different modalities due to noise and conflicts.
method Proposes a deep neural network that multiplicatively combines information from different modalities.
result Consistent accuracy improvements on three multimodal classification tasks.
MMVAE learns multi-modal data with shared and private latent spaces.
problem Learning useful representations across multiple data modalities.
method Mixture-of-experts variational autoencoder (MMVAE).
result MMVAE satisfies four criteria for multi-modal learning.
COBRA reduces modality gap in cross-modal tasks.
problem Joint embedding spaces fail to sufficiently reduce modality gap in multi-modal tasks.
method COBRA trains image and text modalities in a joint fashion using Contrastive Predictive Coding and Noise Contrastive Estimation.
result COBRA significantly reduces the modality gap and generates robust joint-embedding space.
MCC algorithm predicts with partial modalities, outperforming full modalities.
problem Predicting with inconsistent and diverse multi-modal data.
method Instance-oriented Multi-modal Classifier Chains (MCC) algorithm.
result MCC outperforms full modalities in prediction.
Proposes a novel network for CTR prediction by learning modality-specific and modality-invariant representations.
problem Learning good representation of items from multimodal features in E-commerce is challenging due to redundant information across modalities.
method Introduces a Multimodal Adversarial Representation Network (MARN) that calculates modality-specific weights and learns modality-invariant representations.
result Consistently achieves remarkable improvements over state-of-the-art methods in CTR prediction.
We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (VAEs). However, these models typically assume that…
Improves video search by balancing text and visual modalities.
problem Modality imbalance in video search models, focusing mainly on text matching.
method Proposes MBVR with MS samples and DM to balance modalities.
result Empirically shows significant improvement in modality balance and search effectiveness.
New method learns robust joint representations by translating between modalities.
problem Learning robust joint representations from noisy or missing modalities.
method Cyclic translations between modalities with cycle consistency loss.
result Achieves state-of-the-art results on multimodal sentiment analysis datasets.
MoCA uses a novel autoencoder to analyze multi-modal health data.
problem Challenges in analyzing continuous multi-modal health data from wearable devices.
method Proposes MoCA, a self-supervised learning framework combining transformer and masked autoencoder methods.
result Demonstrates strong performance boosts across reconstruction and classification tasks.
We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for…
Obtaining common representations from different modalities is important in that they are interchangeable with each other in a classification problem. For example, we can train a classifier on image features in the common representations and apply it to the testing of the text features in the representations. Existing m…
Framework for handling long-tailed multi-modal data.
problem Class imbalance and long-tailed distributions in multi-modal data.
method Multi-expert architecture with modality-specific networks and dynamic fusion weights.
result Framework outperforms existing methods in long-tailed, class-imbalanced scenarios.
Paper tackles cross-modal anomalies in multi-source data.
problem Detect anomalies in multi-modal data where patterns are inconsistent across different sources.
method Proposes a deep structured anomaly detection framework.
result Demonstrates effectiveness on real-world datasets.
TAP transfers knowledge from unlabeled data to improve cross-modal learning.
problem Improving supervised learning performance using unlabeled data from a different modality.
method Probabilistic approach for missing information estimation, kernel regression, cross-attention module, TAP neural network.
result TAP significantly improves generalization across different domains and neural network architectures.
Study improves product categorization on Amazon using multi-modal fusion.
problem Multi-label product categorization in e-commerce.
method Late fusion of image, description, and title modalities using modified CNN and ResNet-50 models.
result Tri-modal late fusion model achieved an F1 score of 88.2%, significantly better than single modal models. DNN-based cross-modal retrieval has become a research hotspot, by which users can search results across various modalities like image and text. However, existing methods mainly focus on the pairwise correlation and reconstruction error of labeled data. They ignore the semantically similar and dissimilar constraints bet…
JMVAE fails to generate large modalities, JMVAE-kl and JMVAE-h solve this issue.
problem Bi-directional generation between different modalities using VAEs fails for large modalities.
method Proposed JMVAE-kl and JMVAE-h to prevent collapse of joint representation and bi-directional generation.
result JMVAE-kl and JMVAE-h prevent collapse and generate modalities bi-directionally.
This work improves multi-modal generative models by using permutation-invariant neural networks.
problem Improving multi-modal generative models with tighter variational objectives.
method Developed more flexible aggregation schemes based on permutation-invariant neural networks.
result Our variational objective and flexible aggregation models can better approximate the true joint distribution.
A novel cross-modal auto-encoder associates different data types efficiently.
problem Cross-modal data association in heterogeneous datasets.
method Bayesian inference framework with variational auto-encoders and associators.
result Successfully associates visual and auditory data with minimal paired data.
A framework for uncertainty-aware multimodal learning using conformal Shapley intervals.
problem Uncertainty and modality level importance in multimodal learning.
method Introduces conformal Shapley intervals to quantify modality level importance and uncertainty.
result Demonstrates meaningful uncertainty quantification and strong predictive performance.
MAESTRO improves multimodal learning for dynamic time series with adaptive attention and robustness.
problem Challenges in multimodal learning, especially in healthcare and daily living.
method Dynamic intra- and cross-modal interactions, symbolic tokenization, adaptive attention budgeting, sparse cross-modal attention, MoE mechanism.
result Average relative improvements of 4% and 8% over existing multimodal and multivariate approaches, respectively, under complete observations.
Develops multi-modal neural network models for improved prediction and uncertainty quantification.
problem Improving prediction accuracy and uncertainty quantification for multi-modal data.
method Multi-modal Bayesian neural network models with conjugate last-layer estimation using SVI.
result Improved prediction accuracy and uncertainty quantification compared to uni-modal models.
LRMM learns to recommend with missing modalities, improving robustness to data sparsity and cold-start issues.
problem Learning to recommend with missing modalities and cold-start problems.
method LRMM uses modality dropout and multimodal sequential autoencoder to learn multimodal representations and impute missing modalities.
result LRMM achieves state-of-the-art performance on rating prediction tasks and is more robust to data sparsity and cold-start issues.
This paper studies the nonparametric modal regression problem systematically from a statistical learning view. Originally motivated by pursuing a theoretical understanding of the maximum correntropy criterion based regression (MCCR), our study reveals that MCCR with a tending-to-zero scale parameter is essentially moda…
A new method reduces redundancy in multimodal data for improved inference.
problem Understanding and optimizing the contribution of each modality in multimodal tasks.
method Modality-based Tensor Factorization (MRRF) for multimodal fusion.
result Improves multimodal inference tasks by 1% to 4% compared to state-of-the-art.
Symile learns joint representations across multiple modalities, outperforming pairwise CLIP.
problem Pairwise contrastive learning fails to capture joint information between multiple modalities.
method Symile uses a flexible, architecture-agnostic objective to learn modality-specific representations by deriving a lower bound on total correlation.
result Symile outperforms pairwise CLIP on cross-modal classification and retrieval across various datasets.
A method uses autoencoders to align multi-modal neuron data.
problem Inconsistent cell type definitions across different data modalities.
method Coupled training of autoencoders for cross-modal alignment.
result Representations learned by coupled autoencoders can identify single-modality sampled cell types.
ROME improves density estimation for multi-modal, non-normal data.
problem Robust multi-modal density estimation in non-normal, highly correlated distributions.
method ROME uses clustering to segment multi-modal data into uni-modal clusters, then combines KDE estimates for each cluster.
result ROME outperforms state-of-the-art methods and is more robust to various distributions.
FlexCMH learns effective hashing codes from weakly-paired data.
problem Cross-modal hashing assumes perfect correspondence between samples, which is unrealistic.
method FlexCMH uses clustering-based matching to find potential correspondence and jointly optimizes it with hashing functions.
result FlexCMH achieves significantly better results than state-of-the-art methods.
ESE-FN improves elderly activity recognition accuracy.
problem Recognizing individual actions and human-object interactions in elderly activities.
method Exploits multi-modal features from RGB videos and skeleton sequences using ESE attentions and a new Multi-modal Loss.
result ESE-FN achieves best accuracy on ETRI-Activity3D dataset.
New model learns from missing modalities and class labels.
problem Conflict between learning joint representations and modalities in multi-modal data.
method Introduces a novel conditional multi-modal discriminative model using an informative prior distribution and a likelihood-free objective function.
result Our model achieves state-of-the-art results in downstream classification, acoustic inversion, and image and annotation generation.
Proposes a parametric modal regression method using the implicit function theorem.
problem Finding conditional modes for multi-modal conditional distributions.
method Uses the implicit function theorem to develop an objective function for learning a joint function over inputs and targets.
result Empirically demonstrates scalability and effectiveness in learning multi-valued functions and high-dimensional inputs.
A new hashing method handles complex multi-level labels.
problem Handling complex multi-level labels in cross-modal data retrieval.
method Derives a semantic ranking list from feature and label information, integrates semantic ranking into deep cross-modal hashing.
result RDCMH outperforms other methods in cross-modal retrieval applications.
Generative Multisensory Network learns 3D scene representations from multiple modalities.
problem Learning robust 3D scene representations from multiple sensory modalities.
method Amortized Product-of-Experts for efficient inference and cross-modal generation.
result The model can infer modality-invariant 3D scene representations efficiently from various sensory modalities.
Proposes LM3FE for multi-modal feature extraction in image classification.
problem High-dimensional features and multi-modal data challenges.
method Large margin multi-modal multi-task feature extraction (LM3FE) framework.
result LM3FE outperforms single-task feature extraction and multi-modal feature extraction.
Develops a semi-supervised learning method to generate missing neuroimaging modalities.
problem Lack of paired neuroimaging data for training and inference.
method Semi-Supervised Adversarial CycleGAN (SSA-CGAN) using adversarial and cycle losses.
result Improves reconstruction error and robustness to noise.
New model recognizes emotions with missing modalities, improving accuracy.
problem Handling missing modalities in emotion recognition.
method Transformer-based architecture with cross-attention and self-attention mechanisms.
result Improvement of 37% in predicting arousal values and 30% in valence values compared to baseline.
Book reviews multimodal deep learning approaches and models.
problem Understanding and integrating different data types in deep learning.
method Examined current state-of-the-art approaches, discussed transformation and enhancement models, introduced simultaneous handling models, and covered other modalities.
result Unified architectures for handling multiple modalities in deep learning.
New test assesses shared brain activity across different cognitive modalities.
problem Determining if different cognitive modalities use overlapping neural representations.
method Formulated a statistical hypothesis testing approach using permutation testing.
result New test (CMPT) has greater statistical power than cross-modal decoding while maintaining low Type I errors.
Efficient video captioning model captures cross-modal interactions.
problem Capturing frame-level cross-modal interactions in video captioning.
method Proposes High-Order Cross-Modal Attention (HOCA) and Low-Rank HOCA.
result Low-Rank HOCA achieves state-of-the-art performance.
Deep Triplet Networks improve brain imaging modality recognition with limited data.
problem Efficiently recognizing new imaging modalities with scarce training data.
method Few-shot learning model based on Deep Triplet Networks.
result The model outperforms traditional CNN classifiers in modality recognition with limited data.
Proposes a copula-driven framework for multimodal learning.
problem Aligning and fusing representations from multiple modalities with complex interactions.
method Copula model for joint distribution of modalities, Gaussian mixture for marginal distributions.
result Superior performance on public MIMIC datasets.
Inductive graph-based approach for disease classification with incomplete data.
problem Classifying patients with incomplete multi-modal data.
method Multi-modal graph fusion trained end-to-end for node-level classification.
result Outperforms single static graph approach in multi-modal disease classification.
T-EMDE bridges the heterogeneity gap between image and text modalities.
problem Finding similarities between image and text modalities with non-related feature spaces.
method Inspired by EMDE, T-EMDE uses sketches for multimodal operations, avoiding self-attention's quadratic complexity.
result T-EMDE achieves state-of-the-art results and reduces model latency.