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Title: The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision

Paper content:
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them; instead, our model learns by simply looking at images and reading paired questions and answers.
Our model builds an object-based scene representation and translates sentences into executable, symbolic programs.
To bridge the learning of two modules, we use a neuro-symbolic reasoning module that executes these programs on the latent scene representation.
Analogical to human concept learning, the perception module learns visual concepts based on the language description of the object being referred to.
Meanwhile, the learned visual concepts facilitate learning new words and parsing new sentences.
We use curriculum learning to guide the searching over the large compositional space of images and language.
Extensive experiments demonstrate the accuracy and efficiency of our model on learning visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes, compositions, language concepts, scenes and questions, and even new program domains.
It also empowers applications including visual question answering and bidirectional image-text retrieval.
Humans are capable of learning visual concepts by jointly understanding vision and language BID12 BID8 BID15 .
Consider the example shown in Figure 1 -I.
Imagine that someone with no prior knowledge of colors is presented with the images of the red and green cubes, paired with the questions and answers.
They can easily identify the difference in objects' visual appearance (in this case, color), and align it to the corresponding words in the questions and answers (Red and Green).
Other object attributes (e.g., shape) can be learned in a similar fashion.
Starting from there, humans are able to inductively learn the correspondence between visual concepts and word semantics (e.g., spatial relations and referential expressions, Figure 1 -II), and unravel compositional logic from complex questions assisted by the learned visual concepts (Figure 1 -III, also see BID0 ).Motivated
by this, we propose the neuro-symbolic concept learner (NS-CL), which jointly learns visual perception, words, and semantic language parsing from images and question-answer pairs. NS-CL has
three modules: a neural-based perception module that extracts object-level representations from the scene, a visually-grounded semantic parser for translating questions into executable programs, and a symbolic program executor that reads out the perceptual representation of objects, classifies their attributes/relations, and executes the program to obtain an answer.Figure 1: Humans learn visual concepts, words, and semantic parsing jointly and incrementally. I. Learning
visual concepts (red vs. green) starts from looking at simple scenes, reading simple questions, and reasoning over contrastive examples BID12 . II. Afterwards
, we
can interpret referential expressions based on the learned object-based concepts, and learn relational concepts (e.g., on the right of, the same material as). III Finally, we
can interpret complex questions from visual cues by exploiting the compositional structure.NS-CL learns from natural supervision (i.e., images and QA pairs), requiring no annotations on images or semantic programs for sentences. Instead, analogical
to human concept learning, it learns via curriculum learning. NS-CL starts by learning
representations/concepts of individual objects from short questions (e.g., What's the color of the cylinder?) on simple scenes (≤3 objects). By doing so, it learns object-based
concepts such as colors and shapes. NS-CL then learns relational concepts
by leveraging these object-based concepts to interpret object referrals (e.g., Is there a box right of a cylinder?). The model iteratively adapts to more
complex scenes and highly compositional questions.NS-CL's modularized design enables interpretable, robust, and accurate visual reasoning: it achieves state-of-the-art performance on the CLEVR dataset (Johnson et al., 2017a) . More importantly, it naturally learns
disentangled visual and language concepts, enabling combinatorial generalization w.r.t. both visual scenes and semantic programs. In particular, we demonstrate four forms
of generalization. First, NS-CL generalizes to scenes with
more objects and longer semantic programs than those in the training set. Second, it generalizes to new visual attribute
compositions, as demonstrated on the CLEVR-CoGenT (Johnson et al., 2017a) dataset. Third, it enables fast adaptation to novel visual
concepts, such as learning a new color. Finally, the learned visual concepts transfer to
new tasks, such as image-caption retrieval, without any extra fine-tuning.
We presented a method that jointly learns visual concepts, words, and semantic parsing of sentences from natural supervision.
The proposed framework, NS-CL, learns by looking at images and reading paired questions and answers, without any explicit supervision such as class labels for objects.
Our model learns visual concepts with remarkable accuracy.
Based upon the learned concepts, our model achieves good results on question answering, and more importantly, generalizes well to new visual compositions, new visual concepts, and new domain specific languages.The design of NS-CL suggests multiple research directions.
First, constructing 3D object-based representations for realistic scenes needs further exploration BID1 BID5 .
Second, our model assumes a domain-specific language for describing formal semantics.
The integration of formal semantics into the processing of complex natural language would be meaningful future work BID4 Oh et al., 2017) .
We hope our paper could motivate future research in visual concept learning, language learning, and compositionality.Our framework can also be extended to other domains such as video understanding and robotic manipulation.
Here, we would need to discover semantic representations for actions and interactions (e.g., push) beyond static spatial relations.
Along this direction, researchers have studied building symbolic representations for skills (Konidaris et al., 2018) and learning instruction semantics from interaction (Oh et al., 2017) in constrained setups.
Applying neuro-symbolic learning frameworks for concepts and skills would be meaningful future work toward robotic learning in complex interactive environments.

assistant: text: We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns visual concepts, words, and semantic parsing of sentences without explicit supervision on any of them.