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Found 9 papers, 2 papers with code
What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?
With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention.
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models
To this end, we introduce Multi-Modal Low-Rank Adaptation learning (MMLoRA).
ChatDB: Augmenting LLMs with Databases as Their Symbolic Memory
Large language models (LLMs) with memory are computationally universal.
On Uni-Modal Feature Learning in Supervised Multi-Modal Learning
We abstract the features (i. e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions.
Modality Laziness: Everybody's Business is Nobody's Business
We name this problem of multi-modal training, \emph{Modality Laziness}.
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning
In vision-based reinforcement learning (RL) tasks, it is prevalent to assign auxiliary tasks with a surrogate self-supervised loss so as to obtain more semantic representations and improve sample efficiency.
Improving Multi-Modal Learning with Uni-Modal Teachers
We name this problem Modality Failure, and hypothesize that the imbalance of modalities and the implicit bias of common objectives in fusion method prevent encoders of each modality from sufficient feature learning.
Ranked #60 on
Semantic Segmentation
on NYU Depth v2
What Makes Multi-modal Learning Better than Single (Provably)
The world provides us with data of multiple modalities.
Secure Data Sharing With Flow Model
In the classical multi-party computation setting, multiple parties jointly compute a function without revealing their own input data.
