17,258 research outputs found
Imagination Based Sample Construction for Zero-Shot Learning
Zero-shot learning (ZSL) which aims to recognize unseen classes with no
labeled training sample, efficiently tackles the problem of missing labeled
data in image retrieval. Nowadays there are mainly two types of popular methods
for ZSL to recognize images of unseen classes: probabilistic reasoning and
feature projection. Different from these existing types of methods, we propose
a new method: sample construction to deal with the problem of ZSL. Our proposed
method, called Imagination Based Sample Construction (IBSC), innovatively
constructs image samples of target classes in feature space by mimicking human
associative cognition process. Based on an association between attribute and
feature, target samples are constructed from different parts of various
samples. Furthermore, dissimilarity representation is employed to select
high-quality constructed samples which are used as labeled data to train a
specific classifier for those unseen classes. In this way, zero-shot learning
is turned into a supervised learning problem. As far as we know, it is the
first work to construct samples for ZSL thus, our work is viewed as a baseline
for future sample construction methods. Experiments on four benchmark datasets
show the superiority of our proposed method.Comment: Accepted as a short paper in ACM SIGIR 201
Imaginations of WALL-E : Reconstructing Experiences with an Imagination-Inspired Module for Advanced AI Systems
In this paper, we introduce a novel Artificial Intelligence (AI) system
inspired by the philosophical and psychoanalytical concept of imagination as a
``Re-construction of Experiences". Our AI system is equipped with an
imagination-inspired module that bridges the gap between textual inputs and
other modalities, enriching the derived information based on previously learned
experiences. A unique feature of our system is its ability to formulate
independent perceptions of inputs. This leads to unique interpretations of a
concept that may differ from human interpretations but are equally valid, a
phenomenon we term as ``Interpretable Misunderstanding". We employ large-scale
models, specifically a Multimodal Large Language Model (MLLM), enabling our
proposed system to extract meaningful information across modalities while
primarily remaining unimodal. We evaluated our system against other large
language models across multiple tasks, including emotion recognition and
question-answering, using a zero-shot methodology to ensure an unbiased
scenario that may happen by fine-tuning. Significantly, our system outperformed
the best Large Language Models (LLM) on the MELD, IEMOCAP, and CoQA datasets,
achieving Weighted F1 (WF1) scores of 46.74%, 25.23%, and Overall F1 (OF1)
score of 17%, respectively, compared to 22.89%, 12.28%, and 7% from the
well-performing LLM. The goal is to go beyond the statistical view of language
processing and tie it to human concepts such as philosophy and psychoanalysis.
This work represents a significant advancement in the development of
imagination-inspired AI systems, opening new possibilities for AI to generate
deep and interpretable information across modalities, thereby enhancing
human-AI interaction.Comment: 18 pages
Establishing the boundaries: the hippocampal contribution to imagining scenes
When we visualize scenes, either from our own past or invented, we impose a viewpoint for our “mind's eye” and we experience the resulting image as spatially coherent from that viewpoint. The hippocampus has been implicated in this process, but its precise contribution is unknown. We tested a specific hypothesis based on the spatial firing properties of neurons in the hippocampal formation of rats, that this region supports the construction of spatially coherent mental images by representing the locations of the environmental boundaries surrounding our viewpoint. Using functional magnetic resonance imaging, we show that hippocampal activation increases parametrically with the number of enclosing boundaries in the imagined scene. In contrast, hippocampal activity is not modulated by a nonspatial manipulation of scene complexity nor to increasing difficulty of imagining the scenes in general. Our findings identify a specific computational role for the hippocampus in mental imagery and episodic recollection
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