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Found 12 papers, 4 papers with code
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently.
Visual Dependency Transformers: Dependency Tree Emerges from Reversed Attention
When looking at an image, we can decompose the scene into entities and their parts as well as obtain the dependencies between them.
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
Mod-Squad: Designing Mixture of Experts As Modular Multi-Task Learners
To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad').
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study
This work aims to empirically clarify a recently discovered perspective that label smoothing is incompatible with knowledge distillation.
Shot in the Dark: Few-Shot Learning with No Base-Class Labels
To alleviate problems caused by the distribution shift, previous research has explored the use of unlabeled examples from the novel classes, in addition to labeled examples of the base classes, which is known as the transductive setting.
Cross-Supervised Object Detection
After learning a new object category from image-level annotations (with no object bounding boxes), humans are remarkably good at precisely localizing those objects.
Image Deformation Meta-Networks for One-Shot Learning
Humans can robustly learn novel visual concepts even when images undergo various deformations and lose certain information.
Neural Arithmetic Expression Calculator
With curriculum learning, our model can deal with a complex arithmetic expression calculation with the deep hierarchical structure of skill models.
Environment Upgrade Reinforcement Learning for Non-Differentiable Multi-Stage Pipelines
We propose a training algorithm for this framework to address the different training demands of agent and environment.
Multi-level Semantic Feature Augmentation for One-shot Learning
In semantic space, we search for related concepts, which are then projected back into the image feature spaces by the decoder portion of the TriNet.
Learning to score the figure skating sports videos
This paper targets at learning to score the figure skating sports videos.
