This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computervision and patternrecognition (cvpr), 2022. The 3rd ABAW C...
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ISBN:
(数字)9781665487399
ISBN:
(纸本)9781665487399
This paper describes the third Affective Behavior Analysis in-the-wild (ABAW) Competition, held in conjunction with ieee International conference on computervision and patternrecognition (cvpr), 2022. The 3rd ABAW Competition is a continuation of the Competitions held at ICCV 2021, ieee FG 2020 and ieeecvpr 2017 conferences, and aims at automatically analyzing affect. This year the Competition encompasses four Challenges: i) uni-task Valence-Arousal Estimation, ii) uni-task Expression Classification, iii) uni-task Action Unit Detection, and iv) MultiTask-Learning. All the Challenges are based on a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one to be annotated in terms of valence-arousal, expressions and action units. In this paper, we present the four Challenges, with the utilized Competition corpora, we outline the evaluation metrics and present both the baseline systems and the top performing teams' per Challenge. Finally we illustrate the obtained results of the baseline systems and of all participating teams.
recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the complicated structure and uncommon math symbols contained in HMEs. Moreover, the lack of training data is a serious is...
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ISBN:
(数字)9781728193601
ISBN:
(纸本)9781728193601
recognition of Handwritten Mathematical Expressions (HMEs) is a challenging problem because of the complicated structure and uncommon math symbols contained in HMEs. Moreover, the lack of training data is a serious issue, especially for deep learning-based systems. In this paper, we proposed a dual loss attention model that utilizes the existing latex corpus to improve accuracy. The proposed dual loss attention has two losses, including decoder loss and context matching loss to learn semantic invariant features for the encoder and latex grammar for the decoder from handwritten and printed MEs. The results of experiments on the CROHME 2014 and 2016 databases demonstrate the superiority and effectiveness of our proposed model. These results are competitive compared to others reported in recent literature.
We propose a framework that performs action recognition and identity maintenance of multiple targets simultaneously. Instead of first establishing tracks using an appearance model and then performing action recognitio...
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ISBN:
(纸本)9781467312288
We propose a framework that performs action recognition and identity maintenance of multiple targets simultaneously. Instead of first establishing tracks using an appearance model and then performing action recognition, we construct a network flow-based model that links detected bounding boxes across video frames while inferring activities, thus integrating identity maintenance and action recognition. Inference in our model reduces to a constrained minimum cost flow problem, which we solve exactly and efficiently. By leveraging both appearance similarity and action transition likelihoods, our model improves on state-of-the-art results on action recognition for two datasets.
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants sho...
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ISBN:
(纸本)9781728132938
The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling. When it comes to learning interpretable (disentangled) representations, VAE and its variants show unparalleled performance. However, the reasons for this are unclear, since a very particular alignment of the latent embedding is needed but the design of the VAE does not encourage it in any explicit way. We address this matter and offer the following explanation: the diagonal approximation in the encoder together with the inherent stochasticity force local orthogonality of the decoder. The local behavior of promoting both reconstruction and orthogonality matches closely how the PCA embedding is chosen. Alongside providing an intuitive understanding, we justify the statement with full theoretical analysis as well as with experiments.
Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting. We propose a new framework for outf...
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ISBN:
(纸本)9781728171685
Complementary fashion item recommendation is critical for fashion outfit completion. Existing methods mainly focus on outfit compatibility prediction but not in a retrieval setting. We propose a new framework for outfit complementary item retrieval. Specifically, a category-based subspace attention network is presented, which is a scalable approach for learning the subspace attentions. In addition, we introduce an outfit ranking loss that better models the item relationships of an entire outfit. We evaluate our method on the outfit compatibility, FITB and new retrieval tasks. Experimental results demonstrate that our approach outperforms state-of-the-art methods in both compatibility prediction and complementary item retrieval.
Almost all work on texture in the computervision and graphics communities has modeled the texture as tangential, i.e. lying in the tangent plane to the surface. This is equivalent to thinking of the texture as a patt...
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ISBN:
(纸本)0780342364
Almost all work on texture in the computervision and graphics communities has modeled the texture as tangential, i.e. lying in the tangent plane to the surface. This is equivalent to thinking of the texture as a pattern painted on the surface. Three-dimensional textures, where the elements may point out of the surface, have largely been ignored. We study a special class of 3D textures, perpendicular textures where we can model the elements as being normal to the surface. The perspective projection of perpendicularly textured surfaces results in several interesting phenomena, which do not occur in the much-studied tangential texture cease. These include occlusion, foreshortening and illumination. In this paper, we study the geometry of the problem, modeling the locations of the elements of the texture as being a realization of a spatial point process. Relations between slant and tilt of the surface, density and height of elements and occlusions are derived. Occlusions can now be used as a cue to infer shape, instead of being treated as a source of error.
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computervision community will need to face in order to realize its goal of recognizing all object categories. Current stat...
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ISBN:
(纸本)9781467369640
Scaling up fine-grained recognition to all domains of fine-grained objects is a challenge the computervision community will need to face in order to realize its goal of recognizing all object categories. Current state-of-the-art techniques rely heavily upon the use of keypoint or part annotations, but scaling up to hundreds or thousands of domains renders this annotation cost-prohibitive for all but the most important categories. In this work we propose a method for fine-grained recognition that uses no part annotations. Our method is based on generating parts using co-segmentation and alignment, which we combine in a discriminative mixture. Experimental results show its efficacy, demonstrating state-of-the-art results even when compared to methods that use part annotations during training.
We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework o...
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ISBN:
(纸本)0818672587
We study the problem of estimating rigid motion from a sequence of monocular perspective images obtained by navigating around an object while fixating a particular feature point. We cast the problem in the framework of "epipolar geometry", and propose a filter based upon implicit dynamical model for recursively estimating motion under the fixation constraint. This allows us to compare the quality of the estimates directly against the ones obtained assuming a general rigid motion simply by changing the geometry of the parameter space, while maintaining the same structure of the recursive estimator. We also present a closed-form static solution from two views, and a recursive estimator of the relative pose between the viewer and the scene.
This paper presents a completely automated facial action and facial expression recognition system using 2D+3D images recorded in real-time by a structured light sensor. It is based on local feature tracking and rule-b...
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ISBN:
(纸本)9781424439942
This paper presents a completely automated facial action and facial expression recognition system using 2D+3D images recorded in real-time by a structured light sensor. It is based on local feature tracking and rule-based classification of geometric, appearance and surface curvature measurements. Good performance is achieved under relatively non-controlled conditions.
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