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Found 19 papers, 15 papers with code
Talk The Walk: Navigating Grids in New York City through Grounded Dialogue
We introduce `"Talk The Walk", the first large-scale dialogue dataset grounded in action and perception.
The Power of Prompt Tuning for Low-Resource Semantic Parsing
Prompt tuning has recently emerged as an effective method for adapting pre-trained language models to a number of language understanding and generation tasks.
TopiOCQA: Open-domain Conversational Question Answering with Topic Switching
On average, a conversation in our dataset spans 13 question-answer turns and involves four topics (documents).
DuoRAT: Towards Simpler Text-to-SQL Models
Recent neural text-to-SQL models can effectively translate natural language questions to corresponding SQL queries on unseen databases.
Towards Ecologically Valid Research on Language User Interfaces
To this end, we describe what we deem an ideal methodology for machine learning research on LUIs and categorize five common ways in which recent benchmarks deviate from it.
Generative Compositional Augmentations for Scene Graph Prediction
However, test images might contain zero- and few-shot compositions of objects and relationships, e. g. <cup, on, surfboard>.
Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
We show that such models can suffer the most in their ability to generalize to rare compositions, evaluating two different models on the Visual Genome dataset and its more recent, improved version, GQA.
CLOSURE: Assessing Systematic Generalization of CLEVR Models
In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs.
Systematic Generalization: What Is Required and Can It Be Learned?
Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated.
Visual Reasoning with Multi-hop Feature Modulation
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue.
Talk the Walk: Navigating New York City through Grounded Dialogue
We introduce "Talk The Walk", the first large-scale dialogue dataset grounded in action and perception.
FiLM: Visual Reasoning with a General Conditioning Layer
We introduce a general-purpose conditioning method for neural networks called FiLM: Feature-wise Linear Modulation.
Ranked #3 on
Visual Question Answering
on CLEVR-Humans
Image Retrieval with Multi-Modal Query
Visual Question Answering
+1
Learning Visual Reasoning Without Strong Priors
Previous work has operated under the assumption that visual reasoning calls for a specialized architecture, but we show that a general architecture with proper conditioning can learn to visually reason effectively.
Modulating early visual processing by language
It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected.
End-to-end optimization of goal-driven and visually grounded dialogue systems
End-to-end design of dialogue systems has recently become a popular research topic thanks to powerful tools such as encoder-decoder architectures for sequence-to-sequence learning.
GuessWhat?! Visual object discovery through multi-modal dialogue
Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Can deep learning help you find the perfect match?
The answer to this question depends on the personal preferences of the one asking it.
Equilibrated adaptive learning rates for non-convex optimization
Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks.
