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# Fine-Grained Visual Recognition Edit

9 papers with code · Computer Vision

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# Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

17 Jan 2020clovaai/assembled-cnn

Recent studies in image classification have demonstrated a variety of techniques for improving the performance of Convolutional Neural Networks (CNNs).

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# Metric Learning with Adaptive Density Discrimination

18 Nov 2015pumpikano/tf-magnet-loss

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.

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# MILDNet: A Lightweight Single Scaled Deep Ranking Architecture

3 Mar 2019gofynd/mildnet

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.

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# Retrieving Similar E-Commerce Images Using Deep Learning

11 Jan 2019gofynd/mildnet

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.

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# Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition

This paper studies the task of one-shot fine-grained recognition, which suffers from the problem of data scarcity of novel fine-grained classes.

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# X-Linear Attention Networks for Image Captioning

31 Mar 2020Panda-Peter/image-captioning

Recent progress on fine-grained visual recognition and visual question answering has featured Bilinear Pooling, which effectively models the 2$^{nd}$ order interactions across multi-modal inputs.

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# Self-Paced Learning with Adaptive Deep Visual Embeddings

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

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# Multi-Objective Matrix Normalization for Fine-grained Visual Recognition

30 Mar 2020mboboGO/MOMN

In this paper, we propose an efficient Multi-Objective Matrix Normalization (MOMN) method that can simultaneously normalize a bilinear representation in terms of square-root, low-rank, and sparsity.

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# Fine-grained visual recognition with salient feature detection

12 Aug 2018wuyun8210/partdetection

Computer vision based fine-grained recognition has received great attention in recent years.

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