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Compositional Mixture Representations for vision and Text

Compositional Mixture Representations for Vision and Text

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Alaniz, Stephan Federici, Marco Akata, Zeynep Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning. We present a model that learns a shared Gaussian mixture representation imposing the compositionality of the text onto the visual domain without having explicit location supervision. By combining the spatial transformer with a representation learning approach we learn to split images into separately encoded patches to associate visual and textual representations in an interpretable manner. On variations of MNIST and CIFAR10, our model is able to perform weakly supervised object detection and demonstrates its ability to extrapolate to unseen combination of objects.

关键词： Representation learning Visualization Computational modeling Semantics Image retrieval Employment Object detection

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Many-to-many Splatting for Efficient Video Frame Interpolation

Many-to-many Splatting for Efficient Video Frame Interpolati...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Ping Niklaus, Simon Sclaroff, Stan Saenko, Kate Boston Univ Boston MA 02215 USA Adobe Res Berkeley CA USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Motion-based video frame interpolation commonly relies on optical flow to warp pixels from the inputs to the desired interpolation instant. Yet due to the inherent challenges of motion estimation (e.g. occlusions and discontinuities), most state-of-the-art interpolation approaches require subsequent refinement of the warped result to generate satisfying outputs, which drastically decreases the efficiency for multi-frame interpolation. In this work, we propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Specifically, given a frame pair;we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step, and then fuse any overlapping pixels. In doing so, each source pixel renders multiple target pixels and each target pixel can be synthesized from a larger area of visual context. This establishes a many-to-many splatting scheme with robustness to artifacts like holes. Moreover;for each input frame pair, M2M only performs motion estimation once and has a minuscule computational overhead when interpolating an arbitrary number of in-between frames, hence achieving fast multi-frame interpolation. We conducted extensive experiments to analyze M2M, and found that it significantly improves the efficiency while maintaining high effectiveness.

关键词： Interpolation Machine-to-machine communications Visualization computer vision Fuses Motion estimation Benchmark testing

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Learning to Predict Visual Attributes in the Wild

Learning to Predict Visual Attributes in the Wild

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Pham, Khoi Kafle, Kushal Lin, Zhe Ding, Zhihong Cohen, Scott Quan Tran Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781665445092

Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size, posture), and other intrinsic properties (state, action). Existing work is mostly limited to study of attribute prediction in specific domains. In this paper, we introduce a large-scale in-the-wild visual attribute prediction dataset consisting of over 927K attribute annotations for over 260K object instances. Formally, object attribute prediction is a multi-label classification problem where all attributes that apply to an object must be predicted. Our dataset poses significant challenges to existing methods due to large number of attributes, label sparsity, data imbalance, and object occlusion. To this end, we propose several techniques that systematically tackle these challenges, including a base model that utilizes both low- and high-level CNN features with multihop attention, reweighting and resampling techniques, a novel negative label expansion scheme, and a novel supervised attribute-aware contrastive learning algorithm. Using these techniques, we achieve near 3.7 mAP and 5.7 overall F1 points improvement over the current state of the art. Further details about the VAW dataset can be found at https://***/.

关键词： Geometry Visualization computer vision Shape Image color analysis Annotations Prediction algorithms

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Toward Accurate and Realistic Outfits Visualization with Attention to Details

Toward Accurate and Realistic Outfits Visualization with Att...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Kedan Chong, Min Jin Zhang, Jeffrey Liu, Jingen Univ Illinois Urbana IL 61801 USA Revery AI Inc Emeryville CA 94608 USA JD AI Res Mountain View CA USA

ISBN: (纸本)9781665445092

Virtual try-on methods aim to generate images of fashion models wearing arbitrary combinations of garments. This is a challenging task because the generated image must appear realistic and accurately display the interaction between garments. Prior works produce images that are filled with artifacts and fail to capture important visual details necessary for commercial applications. We propose Outfit Visualization Net (OVNet) to capture these important details (e.g. buttons, shading, textures, realistic hemlines, and interactions between garments) and produce high quality multiple-garment virtual try-on images. OVNet consists of 1) a semantic layout generator and 2) an image generation pipeline using multiple coordinated warps. We train the warper to output multiple warps using a cascade loss, which refines each successive warp to focus on poorly generated regions of a previous warp and yields consistent improvements in detail. In addition, we introduce a method for matching outfits with the most suitable model and produce significant improvements for both our and other previous try-on methods. Through quantitative and qualitative analysis, we demonstrate our method generates substantially higher-quality studio images compared to prior works for multi-garment outfits. An interactive interface powered by this method has been deployed on fashion e-commerce websites and received overwhelmingly positive feedback.

关键词： Visualization computer vision Image synthesis Clothing Semantics Pipelines Layout

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FaceFormer: Speech-Driven 3D Facial Animation with Transformers

FaceFormer: Speech-Driven 3D Facial Animation with Transform...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Fan, Yingruo Lin, Zhaojiang Saito, Jun Wang, Wenping Komura, Taku Univ Hong Kong Hong Kong Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Adobe Res San Jose CA USA Texas A&M Univ College Stn TX 77843 USA HKUST Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Speech-driven 31) facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data. Prior works typically focus on learning phoneme-level features of short audio windows with limited context, occasionally resulting in inaccurate lip movements. To tackle this limitation, we propose a TransIbrmer-based autoregressive model, FaceFormet;which encodes the long-term audio context and autoregressively predicts a sequence of animated 3D face meshes, lb cope with the data scarcity issue, we integrate the self-supervised pre-trained speech representations. Also, we devise two biased attention mechanisms well suited to this specific task, including the biased crossmodal multi -head (MN) attention and the biased causal MN self-attention with a periodic positional encoding strategy The 'Omer effectively aligns the audio-motion modalities, whereas the latter offers abilities to generalize to longer audio sequences. Extensive experiments and a perceptual user study show that our approach outperforms the existing state-of-the-arts.

关键词： Geometry Solid modeling Three-dimensional displays Lips Predictive models Transformers pattern recognition

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Hyperspherical Consistency Regularization

Hyperspherical Consistency Regularization

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Tan, Cheng Gao, Zhangyang Wu, Lirong Li, Siyuan Li, Stan Z. Westlake Univ Sch Engn AI Lab Hangzhou Peoples R China Westlake Inst Adv Study Inst Adv Technol Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent advances in contrastive learning have enlightened diverse applications across various semi-supervised fields. Jointly training supervised learning and unsupervised learning with a shared feature encoder becomes a common scheme. Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier. In this work, we systematically explore the relationship between self-supervised learning and supervised learning, and study how self-supervised learning helps robust data-efficient deep learning. We propose hyperspherical consistency regularization (HCR), a simple yet effective plug-and-play method, to regularize the classifier using feature-dependent information and thus avoid bias from labels. Specifically, HCR first project logits from the classifier and feature projections from the projection head on the respective hypersphere, then it enforces data points on hyperspheres to have similar structures by minimizing binary cross entropy of pairwise distances' similarity metrics. Extensive experiments on semi-supervised and weakly-supervised learning demonstrate the effectiveness of our method, by showing superior performance with HCR.

关键词： Deep learning Training Geometry Supervised learning Self-supervised learning Semisupervised learning pattern recognition

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BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View recognition via Perspective Supervision

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Chenyu Chen, Yuntao Tian, Hao Tao, Chenxin Zhu, Xizhou Zhang, Zhaoxiang Huang, Gao Li, Hongyang Qiao, Yu Lu, Lewei Zhou, Jie Dai, Jifeng Tsinghua Univ Beijing Peoples R China Chinese Acad Sci Ctr Artificial Intelligence & Robot HKISI Beijing Peoples R China SenseTime Res Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Beijing Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

We present a novel bird's-eye-view (BEV) detector with perspective supervision, which converges faster and better suits modern image backbones. Existing state-of-the-art BEV detectors are often tied to certain depth pre-trained backbones like VoVNet, hindering the synergy between booming image backbones and BEV detectors. To address this limitation, we prioritize easing the optimization of BEV detectors by introducing perspective view supervision. To this end, we propose a two-stage BEV detector, where proposals from the perspective head are fed into the bird's-eye-view head for final predictions. To evaluate the effectiveness of our model, we conduct extensive ablation studies focusing on the form of supervision and the generality of the proposed detector. The proposed method is verified with a wide spectrum of traditional and modern image backbones and achieves new SoTA results on the large-scale nuScenes dataset. The code shall be released soon.

关键词： detection recognition: Categorization retrieval

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Weak orthogonalization of face and perturbation for recognition

Weak orthogonalization of face and perturbation for recognit...

引用

1998 ieee computer-Society conference on computer vision and pattern recognition

作者： Nagao, K Sohma, M Matsushita Res Inst Tokyo Inc Tama Ku Kawasaki Kanagawa 214 Japan

ISBN: (纸本)0818684976

This paper describes a new method for face recognition under drastic changes of the imaging processes through which the facial images are acquired. Unlike the conventional methods that use only the face features, the present method exploits the statistical information of the variations between the face image sets being compared, in addition to the features of the faces themselves. To incorporate both of the face and perturbation features for recognition, we develop a technique called weak orthogonalization of the two subspaces th at transforms the given two overlapped subspaces so that the volume of the intersection of the resulting two subspaces is minimized. Matching operations are performed in the transformed face space that has thus been weakly orthogonalized against perturbation space. Experimental results on real pictures of the frontal faces from drivers' licenses show that the new algorithm improves the recognition performance over the conventional methods. We also demonstrate the effectiveness of our method on image sets with changes in viewing geometry.

关键词： Feature extraction

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Graph Attention Tracking

Graph Attention Tracking

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Guo, Dongyan Shao, Yanyan Cui, Ying Wang, Zhenhua Zhang, Liyan Shen, Chunhua Zhejiang Univ Technol Hangzhou Peoples R China Nanjing Univ Aeronaut & Astronaut Nanjing Peoples R China Monash Univ Melbourne Vic Australia

ISBN: (纸本)9781665445092

Siamese network based trackers formulate the visual tracking task as a similarity matching problem. Almost all popular Siamese trackers realize the similarity learning via convolutional feature cross-correlation between a target branch and a search branch. However, since the size of target feature region needs to be pre-fixed, these cross-correlation base methods suffer from either reserving much adverse background information or missing a great deal of foreground information. Moreover, the global matching between the target and search region also largely neglects the target structure and part-level information. In this paper, to solve the above issues, we propose a simple target-aware Siamese graph attention network for general object tracking. We propose to establish part-to-part correspondence between the target and the search region with a complete bipartite graph, and apply the graph attention mechanism to propagate target information from the template feature to the search feature. Further, instead of using the pre-fixed region cropping for template-feature-area selection, we investigate a target-aware area selection mechanism to fit the size and aspect ratio variations of different objects. Experiments on challenging benchmarks including GOT-10k, UAV123, OTB-100 and LaSOT demonstrate that the proposed SiamGAT outperforms many stateof-the-art trackers and achieves leading performance.

关键词： Convolutional codes Visualization computer vision Target tracking Benchmark testing Search problems pattern recognition

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Object classification from randomized EEG trials

Object classification from randomized EEG trials

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ahmed, Hamad Wilbur, Ronnie B. Bharadwaj, Hari M. Siskind, Jeffrey Mark Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9781665445092

New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20x) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG.

关键词： Support vector machines Training Recurrent neural networks Training data Electroencephalography pattern recognition Convolutional neural networks

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