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Found 6 papers, 3 papers with code
Decoding the Enigma: Benchmarking Humans and AIs on the Many Facets of Working Memory
Here, we introduce a comprehensive Working Memory (WorM) benchmark dataset for this purpose.
Learning to Learn: How to Continuously Teach Humans and Machines
For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication.
Reason from Context with Self-supervised Learning
To better accommodate the object-centric nature of current downstream tasks such as object recognition and detection, various methods have been proposed to suppress contextual biases or disentangle objects from contexts.
Can Machines Imitate Humans? Integrative Turing Tests for Vision and Language Demonstrate a Narrowing Gap
To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans in three language tasks (Image captioning, Word association, and Conversation) and three vision tasks (Object detection, Color estimation, and Attention prediction).
When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks
On analyzing the task, we find that identifying the target location in the grid world is the main challenge for the models.
On the Efficacy of Co-Attention Transformer Layers in Visual Question Answering
In recent years, multi-modal transformers have shown significant progress in Vision-Language tasks, such as Visual Question Answering (VQA), outperforming previous architectures by a considerable margin.
