Search Results for author: Serge Belongie

Found 98 papers, 53 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.

Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis

no code implementations8 Feb 2022 Flora Yu Shen, Katie Luo, Guandao Yang, Harald Haraldsson, Serge Belongie

In this work, we address an important problem of optical see through (OST) augmented reality: non-negative image synthesis.

Image-to-Image Translation Translation

Stay Positive: Non-Negative Image Synthesis for Augmented Reality

1 code implementation CVPR 2021 Katie Luo, Guandao Yang, Wenqi Xian, Harald Haraldsson, Bharath Hariharan, Serge Belongie

In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image.

Image-to-Image Translation Style Transfer

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.

Classification

Exploring Temporal Granularity in Self-Supervised Video Representation Learning

no code implementations8 Dec 2021 Rui Qian, Yeqing Li, Liangzhe Yuan, Boqing Gong, Ting Liu, Matthew Brown, Serge Belongie, Ming-Hsuan Yang, Hartwig Adam, Yin Cui

The training objective consists of two parts: a fine-grained temporal learning objective to maximize the similarity between corresponding temporal embeddings in the short clip and the long clip, and a persistent temporal learning objective to pull together global embeddings of the two clips.

Representation Learning Self-Supervised Learning

Geometry Processing with Neural Fields

1 code implementation NeurIPS 2021 Guandao Yang, Serge Belongie, Bharath Hariharan, Vladlen Koltun

Most existing geometry processing algorithms use meshes as the default shape representation.

Unsupervised Domain Adaptation: A Reality Check

1 code implementation30 Nov 2021 Kevin Musgrave, Serge Belongie, Ser-Nam Lim

Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms.

Unsupervised Domain Adaptation

Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge

no code implementations15 Nov 2021 Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge Belongie, Alan Yuille, Philip H. S. Torr, Song Bai

To promote the development of occlusion understanding, we collect a large-scale dataset called OVIS for video instance segmentation in the occluded scenario.

Instance Segmentation Object Recognition +3

Fine-Grained Image Analysis with Deep Learning: A Survey

no code implementations11 Nov 2021 Xiu-Shen Wei, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, Serge Belongie

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications.

Fine-Grained Image Recognition Image Retrieval

Learning to Adapt to Semantic Shift

no code implementations29 Sep 2021 Ryan Y Benmalek, Sabhya Chhabria, Pedro O. Pinheiro, Claire Cardie, Serge Belongie

These models outperform both previous work and static models under both \emph{static} and \emph{continual} semantic shifts, suggesting that ``learning to adapt'' is a useful capability for models and agents in a changing world.

Meta-Learning

Robustness and Generalization via Generative Adversarial Training

no code implementations ICCV 2021 Omid Poursaeed, Tianxing Jiang, Harry Yang, Serge Belongie, SerNam Lim

Adversarial training with these examples enable the model to withstand a wide range of attacks by observing a variety of input alterations during training.

Object Detection

Single Image Texture Translation for Data Augmentation

1 code implementation25 Jun 2021 Boyi Li, Yin Cui, Tsung-Yi Lin, Serge Belongie

In this paper, we explore the use of Single Image Texture Translation (SITT) for data augmentation.

Data Augmentation Few-Shot Image Classification +2

The Herbarium 2021 Half-Earth Challenge Dataset

1 code implementation28 May 2021 Riccardo de Lutio, Damon Little, Barbara Ambrose, Serge Belongie

Herbarium sheets present a unique view of the world's botanical history, evolution, and diversity.

When Does Contrastive Visual Representation Learning Work?

no code implementations12 May 2021 Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie

Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.

Contrastive Learning Fine-Grained Image Classification +2

GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds

no code implementations ICCV 2021 Zekun Hao, Arun Mallya, Serge Belongie, Ming-Yu Liu

We represent the world as a continuous volumetric function and train our model to render view-consistent photorealistic images for a user-controlled camera.

Neural Rendering

Occluded Video Instance Segmentation: A Benchmark

1 code implementation2 Feb 2021 Jiyang Qi, Yan Gao, Yao Hu, Xinggang Wang, Xiaoyu Liu, Xiang Bai, Serge Belongie, Alan Yuille, Philip H. S. Torr, Song Bai

On the OVIS dataset, the highest AP achieved by state-of-the-art algorithms is only 16. 3, which reveals that we are still at a nascent stage for understanding objects, instances, and videos in a real-world scenario.

Instance Segmentation Semantic Segmentation +2

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.

PyTorch Metric Learning

1 code implementation20 Aug 2020 Kevin Musgrave, Serge Belongie, Ser-Nam Lim

Deep metric learning algorithms have a wide variety of applications, but implementing these algorithms can be tedious and time consuming.

Metric Learning

Spatiotemporal Contrastive Video Representation Learning

3 code implementations CVPR 2021 Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui

Our representations are learned using a contrastive loss, where two augmented clips from the same short video are pulled together in the embedding space, while clips from different videos are pushed away.

Contrastive Learning Data Augmentation +4

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

A Metric Learning Reality Check

4 code implementations ECCV 2020 Kevin Musgrave, Serge Belongie, Ser-Nam Lim

Deep metric learning papers from the past four years have consistently claimed great advances in accuracy, often more than doubling the performance of decade-old methods.

Metric Learning

Differentiating through the Fréchet Mean

2 code implementations ICML 2020 Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie, Ser-Nam Lim, Christopher De Sa

Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold.

Representation Learning

On Feature Normalization and Data Augmentation

1 code implementation CVPR 2021 Boyi Li, Felix Wu, Ser-Nam Lim, Serge Belongie, Kilian Q. Weinberger

The moments (a. k. a., mean and standard deviation) of latent features are often removed as noise when training image recognition models, to increase stability and reduce training time.

Data Augmentation Domain Generalization +2

Measuring Dataset Granularity

1 code implementation21 Dec 2019 Yin Cui, Zeqi Gu, Dhruv Mahajan, Laurens van der Maaten, Serge Belongie, Ser-Nam Lim

We also investigate the interplay between dataset granularity with a variety of factors and find that fine-grained datasets are more difficult to learn from, more difficult to transfer to, more difficult to perform few-shot learning with, and more vulnerable to adversarial attacks.

Few-Shot Learning

Fine-grained Synthesis of Unrestricted Adversarial Examples

no code implementations20 Nov 2019 Omid Poursaeed, Tianxing Jiang, Yordanos Goshu, Harry Yang, Serge Belongie, Ser-Nam Lim

We propose a novel approach for generating unrestricted adversarial examples by manipulating fine-grained aspects of image generation.

Image Generation Object Detection +1

Neural Puppet: Generative Layered Cartoon Characters

no code implementations4 Oct 2019 Omid Poursaeed, Vladimir G. Kim, Eli Shechtman, Jun Saito, Serge Belongie

We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality.

Frame

Neural Naturalist: Generating Fine-Grained Image Comparisons

no code implementations IJCNLP 2019 Maxwell Forbes, Christine Kaeser-Chen, Piyush Sharma, Serge Belongie

We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds.

Enhancing Adversarial Example Transferability with an Intermediate Level Attack

1 code implementation ICCV 2019 Qian Huang, Isay Katsman, Horace He, Zeqi Gu, Serge Belongie, Ser-Nam Lim

We show that we can select a layer of the source model to perturb without any knowledge of the target models while achieving high transferability.

FoodX-251: A Dataset for Fine-grained Food Classification

1 code implementation14 Jul 2019 Parneet Kaur, Karan Sikka, Weijun Wang, Serge Belongie, Ajay Divakaran

Food classification is a challenging problem due to the large number of categories, high visual similarity between different foods, as well as the lack of datasets for training state-of-the-art deep models.

Classification Fine-Grained Visual Categorization +1

Positional Normalization

2 code implementations NeurIPS 2019 Boyi Li, Felix Wu, Kilian Q. Weinberger, Serge Belongie

A popular method to reduce the training time of deep neural networks is to normalize activations at each layer.

PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows

11 code implementations ICCV 2019 Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, Bharath Hariharan

Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.

Point Cloud Generation Variational Inference

The iMaterialist Fashion Attribute Dataset

1 code implementation13 Jun 2019 Sheng Guo, Weilin Huang, Xiao Zhang, Prasanna Srikhanta, Yin Cui, Yuan Li, Matthew R. Scott, Hartwig Adam, Serge Belongie

The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total.

General Classification Image Classification +1

The Herbarium Challenge 2019 Dataset

no code implementations12 Jun 2019 Kiat Chuan Tan, Yulong Liu, Barbara Ambrose, Melissa Tulig, Serge Belongie

Herbarium sheets are invaluable for botanical research, and considerable time and effort is spent by experts to label and identify specimens on them.

The iMet Collection 2019 Challenge Dataset

1 code implementation3 Jun 2019 Chenyang Zhang, Christine Kaeser-Chen, Grace Vesom, Jennie Choi, Maria Kessler, Serge Belongie

Existing computer vision technologies in artwork recognition focus mainly on instance retrieval or coarse-grained attribute classification.

Fine-Grained Visual Recognition General Classification

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

Adversarial Example Decomposition

no code implementations4 Dec 2018 Horace He, Aaron Lou, Qingxuan Jiang, Isay Katsman, Serge Belongie, Ser-Nam Lim

Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations.

Understanding Image Quality and Trust in Peer-to-Peer Marketplaces

no code implementations26 Nov 2018 Xiao Ma, Lina Mezghani, Kimberly Wilber, Hui Hong, Robinson Piramuthu, Mor Naaman, Serge Belongie

In this work, we conducted a large-scale study on the quality of user-generated images in peer-to-peer marketplaces.

Semantic Segmentation with Scarce Data

no code implementations2 Jul 2018 Isay Katsman, Rohun Tripathi, Andreas Veit, Serge Belongie

Semantic segmentation is a challenging vision problem that usually necessitates the collection of large amounts of finely annotated data, which is often quite expensive to obtain.

Semantic Segmentation

Learning to Evaluate Image Captioning

1 code implementation CVPR 2018 Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, Serge Belongie

To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions.

Data Augmentation Image Captioning

The Neural Painter: Multi-Turn Image Generation

no code implementations16 Jun 2018 Ryan Y. Benmalek, Claire Cardie, Serge Belongie, Xiadong He, Jianfeng Gao

In this work we combine two research threads from Vision/ Graphics and Natural Language Processing to formulate an image generation task conditioned on attributes in a multi-turn setting.

Conditional Image Generation

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

1 code implementation CVPR 2018 Yin Cui, Yang song, Chen Sun, Andrew Howard, Serge Belongie

We propose a measure to estimate domain similarity via Earth Mover's Distance and demonstrate that transfer learning benefits from pre-training on a source domain that is similar to the target domain by this measure.

Fine-Grained Image Classification Fine-Grained Visual Categorization +1

Lean Multiclass Crowdsourcing

no code implementations CVPR 2018 Grant Van Horn, Steve Branson, Scott Loarie, Serge Belongie, Pietro Perona

We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets.

Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors

no code implementations7 Jan 2018 Benjamin Spector, Serge Belongie

Recent work in deep reinforcement learning has allowed algorithms to learn complex tasks such as Atari 2600 games just from the reward provided by the game, but these algorithms presently require millions of training steps in order to learn, making them approximately five orders of magnitude slower than humans.

Atari Games reinforcement-learning +1

Generative Adversarial Perturbations

1 code implementation CVPR 2018 Omid Poursaeed, Isay Katsman, Bicheng Gao, Serge Belongie

In this paper, we propose novel generative models for creating adversarial examples, slightly perturbed images resembling natural images but maliciously crafted to fool pre-trained models.

General Classification Semantic Segmentation

Convolutional Networks with Adaptive Inference Graphs

2 code implementations ECCV 2018 Andreas Veit, Serge Belongie

In this work, we propose convolutional networks with adaptive inference graphs (ConvNet-AIG) that adaptively define their network topology conditioned on the input image.

Separating Self-Expression and Visual Content in Hashtag Supervision

1 code implementation CVPR 2018 Andreas Veit, Maximilian Nickel, Serge Belongie, Laurens van der Maaten

The variety, abundance, and structured nature of hashtags make them an interesting data source for training vision models.

The iNaturalist Species Classification and Detection Dataset

6 code implementations CVPR 2018 Grant Van Horn, Oisin Mac Aodha, Yang song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, Serge Belongie

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.

Classification General Classification +1

Vision-based Real Estate Price Estimation

no code implementations18 Jul 2017 Omid Poursaeed, Tomas Matera, Serge Belongie

Using deep convolutional neural networks on a large dataset of photos of home interiors and exteriors, we develop a method for estimating the luxury level of real estate photos.

Kernel Pooling for Convolutional Neural Networks

no code implementations CVPR 2017 Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, Serge Belongie

We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner.

Face Recognition Fine-Grained Visual Categorization +2

Deep Learning is Robust to Massive Label Noise

no code implementations ICLR 2018 David Rolnick, Andreas Veit, Serge Belongie, Nir Shavit

Deep neural networks trained on large supervised datasets have led to impressive results in image classification and other tasks.

Image Classification

BAM! The Behance Artistic Media Dataset for Recognition Beyond Photography

no code implementations ICCV 2017 Michael J. Wilber, Chen Fang, Hailin Jin, Aaron Hertzmann, John Collomosse, Serge Belongie

Furthermore, we carry out baseline experiments to show the value of this dataset for artistic style prediction, for improving the generality of existing object classifiers, and for the study of visual domain adaptation.

Domain Adaptation

Pose2Instance: Harnessing Keypoints for Person Instance Segmentation

no code implementations4 Apr 2017 Subarna Tripathi, Maxwell Collins, Matthew Brown, Serge Belongie

In a more realistic environment, without the oracle keypoints, the proposed deep person instance segmentation model conditioned on human pose achieves 3. 8% to 10. 5% relative improvements comparing with its strongest baseline of a deep network trained only for segmentation.

Instance Segmentation Semantic Segmentation

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

26 code implementations ICCV 2017 Xun Huang, Serge Belongie

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer.

Style Transfer

Detecting Oriented Text in Natural Images by Linking Segments

6 code implementations CVPR 2017 Baoguang Shi, Xiang Bai, Serge Belongie

It achieves an f-measure of 75. 0% on the standard ICDAR 2015 Incidental (Challenge 4) benchmark, outperforming the previous best by a large margin.

Curved Text Detection

Stacked Generative Adversarial Networks

2 code implementations CVPR 2017 Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, Serge Belongie

In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network.

Conditional Image Generation

Context Matters: Refining Object Detection in Video with Recurrent Neural Networks

no code implementations15 Jul 2016 Subarna Tripathi, Zachary C. Lipton, Serge Belongie, Truong Nguyen

Then we train a recurrent neural network that takes as input sequences of pseudo-labeled frames and optimizes an objective that encourages both accuracy on the target frame and consistency across consecutive frames.

Frame Object Detection

Learning to Match Aerial Images With Deep Attentive Architectures

no code implementations CVPR 2016 Hani Altwaijry, Eduard Trulls, James Hays, Pascal Fua, Serge Belongie

We demonstrate that our models outperform the state-of-the-art on ultra-wide baseline matching and approach human accuracy.

Yum-me: A Personalized Nutrient-based Meal Recommender System

2 code implementations25 May 2016 Longqi Yang, Cheng-Kang Hsieh, Hongjian Yang, Nicola Dell, Serge Belongie, Curtis Cole, Deborah Estrin

We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences.

online learning Recommendation Systems

Residual Networks Behave Like Ensembles of Relatively Shallow Networks

2 code implementations NeurIPS 2016 Andreas Veit, Michael Wilber, Serge Belongie

Moreover, residual networks seem to enable very deep networks by leveraging only the short paths during training.

Conditional Similarity Networks

4 code implementations CVPR 2017 Andreas Veit, Serge Belongie, Theofanis Karaletsos

A main reason for this is that contradicting notions of similarities cannot be captured in a single space.

Can we still avoid automatic face detection?

8 code implementations14 Feb 2016 Michael J. Wilber, Vitaly Shmatikov, Serge Belongie

In this setting, is it still possible for privacy-conscientious users to avoid automatic face detection and recognition?

Face Detection Face Recognition

Detecting Temporally Consistent Objects in Videos through Object Class Label Propagation

no code implementations20 Jan 2016 Subarna Tripathi, Serge Belongie, Youngbae Hwang, Truong Nguyen

We further propose a clustering of VOPs which can efficiently be used for detecting objects in video in a streaming fashion.

Frame Object Detection

Learning Concept Embeddings with Combined Human-Machine Expertise

no code implementations ICCV 2015 Michael J. Wilber, Iljung S. Kwak, David Kriegman, Serge Belongie

This paper presents our work on "SNaCK," a low-dimensional concept embedding algorithm that combines human expertise with automatic machine similarity kernels.

Semantic Video Segmentation : Exploring Inference Efficiency

1 code implementation4 Sep 2015 Subarna Tripathi, Serge Belongie, Youngbae Hwang, Truong Nguyen

We explore the efficiency of the CRF inference beyond image level semantic segmentation and perform joint inference in video frames.

Semantic Segmentation Video Segmentation +1

Bayesian representation learning with oracle constraints

no code implementations16 Jun 2015 Theofanis Karaletsos, Serge Belongie, Gunnar Rätsch

Representation learning systems typically rely on massive amounts of labeled data in order to be trained to high accuracy.

Metric Learning Representation Learning

Learning Deep Representations for Ground-to-Aerial Geolocalization

no code implementations CVPR 2015 Tsung-Yi Lin, Yin Cui, Serge Belongie, James Hays

Most approaches predict the location of a query image by matching to ground-level images with known locations (e. g., street-view data).

Face Verification

Building a Bird Recognition App and Large Scale Dataset With Citizen Scientists: The Fine Print in Fine-Grained Dataset Collection

no code implementations CVPR 2015 Grant Van Horn, Steve Branson, Ryan Farrell, Scott Haber, Jessie Barry, Panos Ipeirotis, Pietro Perona, Serge Belongie

We worked with bird experts to measure the quality of popular datasets like CUB-200-2011 and ImageNet and found class label error rates of at least 4%.

Bird Species Categorization Using Pose Normalized Deep Convolutional Nets

no code implementations11 Jun 2014 Steve Branson, Grant van Horn, Serge Belongie, Pietro Perona

We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification.

Classification Fine-Grained Visual Categorization +1

Similarity Comparisons for Interactive Fine-Grained Categorization

no code implementations CVPR 2014 Catherine Wah, Grant van Horn, Steve Branson, Subhransu Maji, Pietro Perona, Serge Belongie

Current human-in-the-loop fine-grained visual categorization systems depend on a predefined vocabulary of attributes and parts, usually determined by experts.

Fine-Grained Visual Categorization General Classification +1

Microsoft COCO: Common Objects in Context

26 code implementations1 May 2014 Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

Instance Segmentation Object Localization +3

Improving Streaming Video Segmentation with Early and Mid-Level Visual Processing

no code implementations14 Feb 2014 Subarna Tripathi, Youngbae Hwang, Serge Belongie, Truong Nguyen

Despite recent advances in video segmentation, many opportunities remain to improve it using a variety of low and mid-level visual cues.

Motion Segmentation Video Segmentation +1

Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop

no code implementations CVPR 2013 Catherine Wah, Serge Belongie

Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples.

General Classification Zero-Shot Learning

Efficient Large-Scale Structured Learning

no code implementations CVPR 2013 Steve Branson, Oscar Beijbom, Serge Belongie

Our method is shown to be 10-50 times faster than SVMstruct for cost-sensitive multiclass classification while being about as fast as the fastest 1-vs-all methods for multiclass classification.

Classification General Classification +1

Cross-View Image Geolocalization

no code implementations CVPR 2013 Tsung-Yi Lin, Serge Belongie, James Hays

On the other hand, there is no shortage of visual and geographic data that densely covers the Earth we examine overhead imagery and land cover survey data but the relationship between this data and ground level query photographs is complex.

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