- Emergent neural turing machine and its visual navigation🔍
- A large|scale examination of inductive biases shaping high|level ...🔍
- HyperLex Dataset🔍
- Unsupervised learning of mid|level visual representations🔍
- Unsupervised Feature Learning with Emergent Data|Driven ...🔍
- Emergent Correspondence from Image Diffusion🔍
- A self|supervised domain|general learning framework for ...🔍
- Graph neural networks in vision|language image understanding🔍
Emergent Visual|Semantic Hierarchies in Image|Text Representations
Emergent neural turing machine and its visual navigation
Hierarchical representation is helpful in this sense as it abstracts lower level details into noise invariant representations. The lower level representations ...
A large-scale examination of inductive biases shaping high-level ...
The biological visual system transforms patterned light along a hierarchical series of processing stages into a useful visual format, capable of ...
Emergent neural turing machine and its visual navigation - PubMed
... image inputs. Keywords: Autonomous navigation; General-purpose visual learning; Hierarchical representation; Neural network; Universal turing machine.
HyperLex Dataset - Papers With Code
Emergent Visual-Semantic Hierarchies in Image-Text Representations. Hadar Averbuch-Elor, Morris Alper. 10 Jul 2024. 21. Relational Word Embeddings. Steven ...
Unsupervised learning of mid-level visual representations
This study introduces Contrastive Local and Predictive Plasticity (CLAPP), a self-supervised local learning rule for deep hierarchical image representations ...
Unsupervised Feature Learning with Emergent Data-Driven ...
In summary, HACK can discover prototypicality and also organize the images based on their semantic and hierarchical structure. ... Cognitive representations of ...
Emergent Correspondence from Image Diffusion
Momentum contrast for unsupervised visual representation learning. In CVPR ... Unleashing text-to-image diffusion models for visual perception. arXiv ...
A self-supervised domain-general learning framework for ... - bioRxiv
which learn a hierarchy of visual feature spaces that (i) have emergent categorization capacity based ... visual object representations in a population of ...
Graph neural networks in vision-language image understanding
Hierarchical spatial (Tree). These graphs build on from the spatial graph but the relationships between nodes focus on the hierarchical nature ...
Contrastive learning explains the emergence and function of visual ...
We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning ...
On visual representations in an emergent language game
References (15) ; Categorical perception. Article. Full-text available. Jan 2010 ; ImageNet: a Large-Scale Hierarchical Image Database. Conference Paper. Full- ...
We examined whether visual neural networks align with brain representations due to shared constraints or universal features. Analysis of diverse networks ...
Evidence for Hypodescent in Visual Semantic AI - Mahzarin R. Banaji
the valence of a visual semantic representation of an encoded image, ... Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv.
Exploring a Sequence Model Trained on a Synthetic Task
Although the network has no a priori knowledge of the game or its rules, it is uncovered evidence of an emergent nonlinear internal representation of the ...
... Text and Image for Image Retrieval-An Empirical Odyssey. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019 [arXiv] [Code]; Chen Sun, Per ...
Evidence for Hypodescent in Visual Semantic AI - ACM Digital Library
... visual semantic representation of an encoded image, rather than a word. The ... Hierarchical Text-Conditional Image Generation with CLIP Latents. arXiv ...
Hierarchical Text Classification and Its Foundations: A Review of ...
This “layer specialization” phenomenon suggests that stacking attention layers creates expressive representations that blend morphological and grammatical ...
Track: Poster Session 5 - ICLR 2025
Modern semantic segmentation methods devote much effect to adjusting image feature representations ... image and text classification benchmarks. In-Person ...
UNSUPERVISED FEATURE LEARNING WITH EMERGENT DATA ...
Given a set of images, thus it is desirable to organize them based on prototypicality to form a visual hierarchy. If the image feature is given, it is ...
Characterizing emergent representations in a space of candidate ...
The reverse hierarchy theory of visual perceptual learning. Trends in Cognitive Sciences, 8(10):457–464, 2004. 9. Page 10. [2] L. Aitchison and M. Lengyel ...