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Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
[article]
2015
arXiv
pre-print
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We present a simple deep learning framework to simultaneously predict keypoint locations and their respective visibilities and use those to achieve state-of-the-art performance for fine-grained classification ...
We demonstrate the effectiveness of our accurate keypoint localization and visibility prediction on the fine-grained bird recognition task with and without ground truth bird bounding boxes, and outperform ...
In addition, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPUs used for this research. ...
arXiv:1507.06332v1
fatcat:okkekpyemje4vnnbrkbbm4dfea
Part Localization using Multi-Proposal Consensus for Fine-Grained Categorization
2015
Procedings of the British Machine Vision Conference 2015
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Next, we demonstrate their effectiveness in the fine-grained categorization task by using the predicted keypoints to align head and torso regions, then extracting finetuned AlexNet features from the localized ...
For effective fine-grained category detection, the keypoint localization methods must have high accuracy, low false positive rates, and low false negative rates. ...
Next, we demonstrate their effectiveness in the fine-grained categorization task by using the predicted keypoints to align head and torso regions, then extracting finetuned AlexNet features from the localized ...
doi:10.5244/c.29.128
dblp:conf/bmvc/ShihMSH15
fatcat:arru5bcehrb5rldrtpuv3yhjqy
Aligned to the Object, not to the Image: A Unified Pose-aligned Representation for Fine-grained Recognition
[article]
2018
arXiv
pre-print
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The effectiveness of this paradigm relative to competing methods suggests the critical importance of disentangling pose and appearance for continued progress in fine-grained recognition. ...
Dramatic appearance variation due to pose constitutes a great challenge in fine-grained recognition, one which recent methods using attention mechanisms or second-order statistics fail to adequately address ...
Introduction What makes fine-grained visual categorization (FGVC), commonly referred to as fine-grained recognition, different from general visual categorization? ...
arXiv:1801.09057v4
fatcat:nwuvizkmebfbrhonnkfwkuhxdy
Classifying Object Manipulation Actions based on Grasp-types and Motion-Constraints
[article]
2018
arXiv
pre-print
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Our results justifies the efficacy of grasp attributes for the task of fine-grained and coarse-grained object manipulation action recognition. ...
We propose to use grasp and motion-constraints information to recognise and understand action intention with different objects. ...
Grasp attributes We propose to use coarse and fine level categorization of grasp types. ...
arXiv:1806.07574v1
fatcat:4vtq63rgxbbkjlr5zbalw2oufe
Towards Scene Understanding with Detailed 3D Object Representations
2014
International Journal of Computer Vision
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The fine-grained model, in conjunction with the explicit 3D scene model, further allows one to infer part-level occlusions between the modeled objects, as well as occlusions by other, unmodeled scene elements ...
An object class - in our case cars - is modeled as a deformable 3D wireframe, which enables fine-grained modeling at the level of individual vertices and faces. ...
, part occlusion estimation, fine-grained categorization, and even ultra-wide baseline matching. ...
doi:10.1007/s11263-014-0780-y
fatcat:dtpfsces6jbj7lyckysrnlrgxa
Effectiveness of Grasp Attributes and Motion-Constraints for Fine-Grained Recognition of Object Manipulation Actions
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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We propose to leverage grasp and motion-constraints information, using a suitable representation, to recognize and understand action intention with different objects. ...
The evaluation involves a) Different contemporary multi-class classifiers, and binary classifiers with one-vsone multi-class voting scheme, and b) Differential comparisons results based on subsets of attributes ...
This analysis also helps to demonstrate that different classification frameworks, largely arrive at a consensus with respect to our hypothesis about using grasp and motion-constraints for fine-grained ...
doi:10.1109/cvprw.2016.156
dblp:conf/cvpr/GuptaBB16
fatcat:7hjrhcaaunb75ax5tfbnqtz7pu
Label Relation Graphs Enhanced Hierarchical Residual Network for Hierarchical Multi-Granularity Classification
[article]
2022
arXiv
pre-print
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Experiments on three commonly used datasets demonstrate the effectiveness of our approach compared to the state-of-the-art HMC approaches and fine-grained visual classification (FGVC) methods exploiting ...
If the observed label is at the leaf level, the combinatorial loss further imposes the multi-class cross-entropy loss to increase the weight of fine-grained classification loss. ...
HMC with local multi-layer perceptrons (HMC-LMLP) [3] proposed to train a chain of multi-layer perceptron (MLP) networks, each corresponding to a hierarchical level. ...
arXiv:2201.03194v2
fatcat:vwnkqsqnhjbqbfqpszeyrwwilu
Synergistic Instance-Level Subspace Alignment for Fine-Grained Sketch-Based Image Retrieval
2017
IEEE Transactions on Image Processing
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We study the problem of fine-grained sketch-based image retrieval. ...
To reduce the sketch-image gap when comparing low-level features, we also (iii) propose a novel method for instance-level domain-alignment, that exploits both subspace and instance-level cues to better ...
Annotation To annotate our final fine-grained SBIR dataset, we again use crowdsourcing for both fine-grained attributes as well as parts which we will later use for strongly-supervised DPM training. ...
doi:10.1109/tip.2017.2745106
pmid:28858796
fatcat:dhp2a73iyvg67kk7yu5z2x6u7m
Effective Domain Knowledge Transfer with Soft Fine-tuning
[article]
2019
arXiv
pre-print
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Furthermore, we propose a novel and light-weighted method, namely soft fine-tuning. ...
Convolutional neural networks require numerous data for training. ...
While in general there is a consensus that large-scale labeled datasets are needed to train CNNs with millions of learnable parameters, for some specific tasks, e.g. fine-grained categorization and infrared ...
arXiv:1909.02236v1
fatcat:nivysmbopfa25nsfz2cxa7psta
Multiple Sound Sources Localization from Coarse to Fine
[article]
2020
arXiv
pre-print
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We then employ the localization results for sound separation and obtain comparable performance to existing methods. ...
Code is available at https://github.com/shvdiwnkozbw/Multi-Source-Sound-Localization ...
We use them for fine-grained feature alignment in next step. Fine-Grained Audiovisual Alignment. ...
arXiv:2007.06355v2
fatcat:36e5p5bcqbc6jdorflzkvrdfr4
Super-Fine Attributes with Crowd Prototyping
2018
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Inspired by fine-grained and relative attributes, we introduce super-fine attributes, which now describe multiple, integral concepts of a single trait as multi-dimensional perceptual coordinates. ...
Crowd prototyping facilitates efficient crowdsourcing of super-fine labels by pre-discovering salient perceptual concepts for prototype matching. ...
Proposal. We introduce super-fine attributes. Similar to fine-grained and relative attributes, super-fine attributes describe both clear and subtle discriminations between images. ...
doi:10.1109/tpami.2018.2836900
pmid:29994759
fatcat:z7cf52y4jrdmnl7fke5yvekshe
Grapy-ML: Graph Pyramid Mutual Learning for Cross-Dataset Human Parsing
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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By making use of the multi-granularity labels, Grapy-ML learns a more discriminative feature representation and achieves state-of-the-art performance, which is demonstrated by extensive experiments on ...
Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. ...
GCN represents using graph convolutions for graph context reasoning in the proposed GPM. GCR represents using self-attention for graph context reasoning in the proposed GPM. ...
doi:10.1609/aaai.v34i07.6728
fatcat:juvtu6haingcxgoaabsydtbfaa
A Gated Peripheral-Foveal Convolutional Neural Network for Unified Image Aesthetic Prediction
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1812.07989v1 fatcat:3rrs346axnhqfioufwossi5gr4
The peripheral vision is used for perceiving the broad spatial scene and selecting the attended regions for the fovea. ...
Learning fine-grained details is a key issue in image aesthetic assessment. ...
[18] proposed a multi-patch aggregation network (DMA-Net) to extract local fine-grained features from multiple randomly cropped patches. ...
arXiv:1812.07989v2
fatcat:6qvnf6ex6nd4fesdiccndf43jq
Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing
[article]
2019
arXiv
pre-print
Preserved Fulltext
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By making use of the multi-granularity labels, Grapy-ML learns a more discriminative feature representation and achieves state-of-the-art performance, which is demonstrated by extensive experiments on ...
Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. ...
GCN represents using graph convolutions for graph context reasoning in the proposed GPM. ...
arXiv:1911.12053v1
fatcat:ugfuo5dp35hr7i5lpkrnrhfc6m
Who Likes What? — SplitLBI in Exploring Preferential Diversity of Ratings
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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Such a multi-level model, enables us to simultaneously learn a coarse-grained social preference function together with a fine-grained personalized diversity. ...
It provides us prediction power for the choices of new users on new alternatives. ...
Methodology In this section, we systematically introduce the methodology for coarse-to-fine grained preference learning. ...
doi:10.1609/aaai.v34i01.5359
fatcat:bz5yhmf3bzahjamc2od75q3ayq
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