Fine-Grained Visual Recognition

23 papers with code • 0 benchmarks • 5 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Fine-Grained Visual Recognition models and implementations
2 papers

Most implemented papers

Bilinear CNNs for Fine-grained Visual Recognition

tommarvoloriddle/Bilinear-CNN-Tensorflow2.4-implementation 29 Apr 2015

We then present a systematic analysis of these networks and show that (1) the bilinear features are highly redundant and can be reduced by an order of magnitude in size without significant loss in accuracy, (2) are also effective for other image classification tasks such as texture and scene recognition, and (3) can be trained from scratch on the ImageNet dataset offering consistent improvements over the baseline architecture.

Retrieving Similar E-Commerce Images Using Deep Learning

gofynd/mildnet 11 Jan 2019

In this paper, we propose a deep convolutional neural network for learning the embeddings of images in order to capture the notion of visual similarity.

MILDNet: A Lightweight Single Scaled Deep Ranking Architecture

gofynd/mildnet 3 Mar 2019

Inspired by the fact that successive CNN layers represent the image with increasing levels of abstraction, we compressed our deep ranking model to a single CNN by coupling activations from multiple intermediate layers along with the last layer.

Deep CNNs Meet Global Covariance Pooling: Better Representation and Generalization

jiangtaoxie/fast-MPN-COV 15 Apr 2019

The proposed methods are highly modular, readily plugged into existing deep CNNs.

Fashionpedia: Ontology, Segmentation, and an Attribute Localization Dataset

tensorflow/tpu ECCV 2020

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).

Metric Learning with Adaptive Density Discrimination

pumpikano/tf-magnet-loss 18 Nov 2015

Beyond classification, we further validate the saliency of the learnt representations via their attribute concentration and hierarchy recovery properties, achieving 10-25% relative gains on the softmax classifier and 25-50% on triplet loss in these tasks.

Feathers dataset for Fine-Grained Visual Categorization

feathers-dataset/feathersv1-dataset 18 Apr 2020

This paper introduces a novel dataset FeatherV1, containing 28, 272 images of feathers categorized by 595 bird species.

CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

KeremTurgutlu/self_supervised Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2021

Existing computer vision research in artwork struggles with artwork's fine-grained attributes recognition and lack of curated annotated datasets due to their costly creation.

Self-Paced Learning with Adaptive Deep Visual Embeddings

vithursant/SPL-ADVisE 24 Jul 2018

Selecting the most appropriate data examples to present a deep neural network (DNN) at different stages of training is an unsolved challenge.

Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

ChaojianYu/Hierarchical-Bilinear-Pooling ECCV 2018

Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning.