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09/13/2023
Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?
Convolutional networks and vision transformers have different forms of p...
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04/27/2023
Adaptive manifold for imbalanced transductive few-shot learning
Transductive few-shot learning algorithms have showed substantially supe...
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03/16/2023
PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervision
Impressive progress in generative models and implicit representations ga...
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01/17/2023
Opti-CAM: Optimizing saliency maps for interpretability
Methods based on class activation maps (CAM) provide a simple mechanism ...
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10/21/2022
Boosting vision transformers for image retrieval
Vision transformers have achieved remarkable progress in vision tasks su...
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06/29/2022
Teach me how to Interpolate a Myriad of Embeddings
Mixup refers to interpolation-based data augmentation, originally motiva...
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03/23/2022
What to Hide from Your Students: Attention-Guided Masked Image Modeling
Transformers and masked language modeling are quickly being adopted and ...
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07/16/2021
All the attention you need: Global-local, spatial-channel attention for image retrieval
We address representation learning for large-scale instance-level image ...
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06/09/2021
Tensor feature hallucination for few-shot learning
Few-shot classification addresses the challenge of classifying examples ...
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06/09/2021
It Takes Two to Tango: Mixup for Deep Metric Learning
Metric learning involves learning a discriminative representation such t...
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04/19/2021
Few-shot learning via tensor hallucination
Few-shot classification addresses the challenge of classifying examples ...
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03/29/2021
AlignMix: Improving representation by interpolating aligned features
Mixup is a powerful data augmentation method that interpolates between t...
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03/26/2021
On the hidden treasure of dialog in video question answering
High-level understanding of stories in video such as movies and TV shows...
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01/05/2021
Local Propagation for Few-Shot Learning
The challenge in few-shot learning is that available data is not enough ...
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12/14/2020
Iterative label cleaning for transductive and semi-supervised few-shot learning
Few-shot learning amounts to learning representations and acquiring know...
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06/29/2020
Asymmetric metric learning for knowledge transfer
Knowledge transfer from large teacher models to smaller student models h...
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02/18/2020
Few-Shot Few-Shot Learning and the role of Spatial Attention
Few-shot learning is often motivated by the ability of humans to learn n...
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12/04/2019
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Adversarial examples of deep neural networks are receiving ever increasi...
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12/01/2019
Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images
Weakly-supervised object detection attempts to limit the amount of super...
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11/19/2019
Rethinking deep active learning: Using unlabeled data at model training
Active learning typically focuses on training a model on few labeled exa...
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10/01/2019
Graph convolutional networks for learning with few clean and many noisy labels
In this work we consider the problem of learning a classifier from noisy...
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05/15/2019
Local Features and Visual Words Emerge in Activations
We propose a novel method of deep spatial matching (DSM) for image retri...
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04/09/2019
Label Propagation for Deep Semi-supervised Learning
Semi-supervised learning is becoming increasingly important because it c...
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03/28/2019
Smooth Adversarial Examples
This paper investigates the visual quality of the adversarial examples. ...
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03/12/2019
Dense Classification and Implanting for Few-Shot Learning
Training deep neural networks from few examples is a highly challenging ...
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07/23/2018
Hybrid Diffusion: Spectral-Temporal Graph Filtering for Manifold Ranking
State of the art image retrieval performance is achieved with CNN featur...
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03/29/2018
Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking
In this paper we address issues with image retrieval benchmarking on sta...
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03/29/2018
Mining on Manifolds: Metric Learning without Labels
In this work we present a novel unsupervised framework for hard training...
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09/14/2017
Unsupervised deep object discovery for instance recognition
Severe background clutter is challenging in many computer vision tasks, ...
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03/20/2017
Fast Spectral Ranking for Similarity Search
Despite the success of deep learning on representing images for particul...
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11/16/2016
Efficient Diffusion on Region Manifolds: Recovering Small Objects with Compact CNN Representations
Query expansion is a popular method to improve the quality of image retr...
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11/14/2016