4,543 research outputs found
Literal Perceptual Inference
In this paper, I argue that theories of perception that appeal to Helmholtz’s idea of unconscious inference (“Helmholtzian” theories) should be taken literally, i.e. that the inferences appealed to in such theories are inferences in the full sense of the term, as employed elsewhere in philosophy and in ordinary discourse.
In the course of the argument, I consider constraints on inference based on the idea that inference is a deliberate acton, and on the idea that inferences depend on the syntactic structure of representations. I argue that inference is a personal-level but sometimes unconscious process that cannot in general be distinguished from association on the basis of the structures of the representations over which it’s defined. I also critique arguments against representationalist interpretations of Helmholtzian theories, and argue against the view that perceptual inference is encapsulated in a module
Curriculum Learning for Compositional Visual Reasoning
Visual Question Answering (VQA) is a complex task requiring large datasets
and expensive training. Neural Module Networks (NMN) first translate the
question to a reasoning path, then follow that path to analyze the image and
provide an answer. We propose an NMN method that relies on predefined
cross-modal embeddings to ``warm start'' learning on the GQA dataset, then
focus on Curriculum Learning (CL) as a way to improve training and make a
better use of the data. Several difficulty criteria are employed for defining
CL methods. We show that by an appropriate selection of the CL method the cost
of training and the amount of training data can be greatly reduced, with a
limited impact on the final VQA accuracy. Furthermore, we introduce
intermediate losses during training and find that this allows to simplify the
CL strategy
Multimodal Representations for Teacher-Guided Compositional Visual Reasoning
Neural Module Networks (NMN) are a compelling method for visual question
answering, enabling the translation of a question into a program consisting of
a series of reasoning sub-tasks that are sequentially executed on the image to
produce an answer. NMNs provide enhanced explainability compared to integrated
models, allowing for a better understanding of the underlying reasoning
process. To improve the effectiveness of NMNs we propose to exploit features
obtained by a large-scale cross-modal encoder. Also, the current training
approach of NMNs relies on the propagation of module outputs to subsequent
modules, leading to the accumulation of prediction errors and the generation of
false answers. To mitigate this, we introduce an NMN learning strategy
involving scheduled teacher guidance. Initially, the model is fully guided by
the ground-truth intermediate outputs, but gradually transitions to an
autonomous behavior as training progresses. This reduces error accumulation,
thus improving training efficiency and final performance.We demonstrate that by
incorporating cross-modal features and employing more effective training
techniques for NMN, we achieve a favorable balance between performance and
transparency in the reasoning process
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