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Found 9 papers, 3 papers with code
Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly performant multilingual models that match or surpass the capabilities of monolingual models.
Aya 23: Open Weight Releases to Further Multilingual Progress
This technical report introduces Aya 23, a family of multilingual language models.
A Generalization Theory of Cross-Modality Distillation with Contrastive Learning
For instance, one typically aims to minimize the L2 distance or contrastive loss between the learned features of pairs of samples in the source (e. g. image) and the target (e. g. sketch) modalities.
Mitigating the Alignment Tax of RLHF
Building on the analysis and the observation that averaging different layers of the transformer leads to significantly different alignment-forgetting trade-offs, we propose Heterogeneous Model Averaging (HMA) to Heterogeneously find various combination ratios of model layers.
Sketch-BERT: Learning Sketch Bidirectional Encoder Representation from Transformers by Self-supervised Learning of Sketch Gestalt
Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches.
Domain-Aware SE Network for Sketch-based Image Retrieval with Multiplicative Euclidean Margin Softmax
This paper proposes a novel approach for Sketch-Based Image Retrieval (SBIR), for which the key is to bridge the gap between sketches and photos in terms of the data representation.
Instance-level Sketch-based Retrieval by Deep Triplet Classification Siamese Network
Additionally, to study the tasks of sketch-based hairstyle retrieval, this paper contributes a new instance-level photo-sketch dataset - Hairstyle Photo-Sketch dataset, which is composed of 3600 sketches and photos, and 2400 sketch-photo pairs.
VOCABULARY-INFORMED VISUAL FEATURE AUGMENTATION FOR ONE-SHOT LEARNING
Besides directly augmenting image features, we transform the image features to semantic space using the encoder and perform the data augmentation.
Verb Pattern: A Probabilistic Semantic Representation on Verbs
In this paper, we introduce verb patterns to represent verbs' semantics, such that each pattern corresponds to a single semantic of the verb.
