Search Results for author: Menglin Jia

Found 9 papers, 7 papers with code

Visual Prompt Tuning

no code implementations23 Mar 2022 Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim

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

1 code implementation15 Dec 2021 Menglin Jia, Bor-Chun Chen, Zuxuan Wu, Claire Cardie, Serge Belongie, Ser-Nam Lim

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

1 code implementation Findings (EMNLP) 2021 Menglin Jia, Austin Reiter, Ser-Nam Lim, Yoav Artzi, Claire Cardie

We introduce Classification with Alternating Normalization (CAN), a non-parametric post-processing step for classification.

Intentonomy: a Dataset and Study towards Human Intent Understanding

1 code implementation CVPR 2021 Menglin Jia, Zuxuan Wu, Austin Reiter, Claire Cardie, Serge Belongie, Ser-Nam Lim

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

3 code implementations ECCV 2020 Menglin Jia, Mengyun Shi, Mikhail Sirotenko, Yin Cui, Claire Cardie, Bharath Hariharan, Hartwig Adam, Serge Belongie

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

no code implementations3 Mar 2020 Austin Reiter, Menglin Jia, Pu Yang, Ser-Nam Lim

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

7 code implementations CVPR 2019 Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang song, Serge Belongie

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.

Image Classification Long-tail Learning

A Deep-Learning-Based Fashion Attributes Detection Model

1 code implementation24 Oct 2018 Menglin Jia, Yichen Zhou, Mengyun Shi, Bharath Hariharan

Such information analyzing process is called abstracting, which recognize similarities or differences across all the garments and collections.

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