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Found 5 papers, 1 papers with code
Multi-modal Situated Reasoning in 3D Scenes
Comprehensive evaluations on MSQA and MSNN highlight the limitations of existing vision-language models and underscore the importance of handling multi-modal interleaved inputs and situation modeling.
An Embodied Generalist Agent in 3D World
However, several significant challenges remain: (i) most of these models rely on 2D images yet exhibit a limited capacity for 3D input; (ii) these models rarely explore the tasks inherently defined in 3D world, e. g., 3D grounding, embodied reasoning and acting.
Bayesian Evidential Learning for Few-Shot Classification
Few-Shot Classification(FSC) aims to generalize from base classes to novel classes given very limited labeled samples, which is an important step on the path toward human-like machine learning.
Memory-Based Label-Text Tuning for Few-Shot Class-Incremental Learning
The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem.
class-incremental learning
Few-Shot Class-Incremental Learning
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Switchable Representation Learning Framework with Self-compatibility
Existing research mainly focuses on the one-to-one compatible paradigm, which is limited in learning compatibility among multiple models.
