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Found 9 papers, 7 papers with code
Visual Prompt Tuning
The current modus operandi in adapting pre-trained models involves updating all the backbone parameters, ie, full fine-tuning.
Rethinking Nearest Neighbors for Visual Classification
In this paper, we investigate $k$-Nearest-Neighbor (k-NN) classifiers, a classical model-free learning method from the pre-deep learning era, as an augmentation to modern neural network based approaches.
When in Doubt: Improving Classification Performance with Alternating Normalization
We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification.
Exploring Visual Engagement Signals for Representation Learning
Visual engagement in social media platforms comprises interactions with photo posts including comments, shares, and likes.
Intentonomy: a Dataset and Study towards Human Intent Understanding
Based on our findings, we conduct further study to quantify the effect of attending to object and context classes as well as textual information in the form of hashtags when training an intent classifier.
Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset
In this work we explore the task of instance segmentation with attribute localization, which unifies instance segmentation (detect and segment each object instance) and fine-grained visual attribute categorization (recognize one or multiple attributes).
Fine-Grained Visual Categorization
Fine-Grained Visual Recognition
+3
Deep Multi-Modal Sets
Most deep learning-based methods rely on a late fusion technique whereby multiple feature types are encoded and concatenated and then a multi layer perceptron (MLP) combines the fused embedding to make predictions.
Class-Balanced Loss Based on Effective Number of Samples
We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss.
Ranked #2 on
Long-tail Learning
on EGTEA
A Deep-Learning-Based Fashion Attributes Detection Model
Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.
