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Found 7 papers, 1 papers with code
They Are Not All Alike: Answering Different Spatial Questions Requires Different Grounding Strategies
By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
A Small but Informed and Diverse Model: The Case of the Multimodal GuessWhat!? Guessing Game
Pre-trained Vision and Language Transformers achieve high performance on downstream tasks due to their ability to transfer representational knowledge accumulated during pretraining on substantial amounts of data.
Demonstrating EMMA: Embodied MultiModal Agent for Language-guided Action Execution in 3D Simulated Environments
We demonstrate EMMA, an embodied multimodal agent which has been developed for the Alexa Prize SimBot challenge.
Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision
This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities.
Psycholinguistics meets Continual Learning: Measuring Catastrophic Forgetting in Visual Question Answering
We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA).
Grounded Textual Entailment
Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics.
Ranked #2 on
Natural Language Inference
on V-SNLI
Iterative Multi-document Neural Attention for Multiple Answer Prediction
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.
