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Learning from Unique Perspectives: User-aware Saliency Modeling

Learning from Unique Perspectives: User-aware Saliency Model...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Shi Valliappa, Nachiappan Shen, Shaolei Ye, Xinyu Kohlhoff, Kai He, Junfeng Univ Minnesota Minneapolis MN USA Google Res Mountain View CA USA Google Mountain View CA USA

ISBN: (纸本)9798350301298

Everyone is unique. Given the same visual stimuli, people's attention is driven by both salient visual cues and their own inherent preferences. Knowledge of visual preferences not only facilitates understanding of fine-grained attention patterns of diverse users, but also has the potential of benefiting the development of customized applications. Nevertheless, existing saliency models typically limit their scope to attention as it applies to the general population and ignore the variability between users' behaviors. In this paper, we identify the critical roles of visual preferences in attention modeling, and for the first time study the problem of user-aware saliency modeling. Our work aims to advance attention research from three distinct perspectives: (1) We present a new model with the flexibility to capture attention patterns of various combinations of users, so that we can adaptively predict personalized attention, user group attention, and general saliency at the same time with one single model;(2) To augment models with knowledge about the composition of attention from different users, we further propose a principled learning method to understand visual attention in a progressive manner;and (3) We carry out extensive analyses on publicly available saliency datasets to shed light on the roles of visual preferences. Experimental results on diverse stimuli, including naturalistic images and web pages, demonstrate the advantages of our method in capturing the distinct visual behaviors of different users and the general saliency of visual stimuli.

关键词： Low-level vision

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Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes

Stereo Radiance Fields (SRF): Learning View Synthesis for Sp...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chibane, Julian Bansal, Aayush Lazova, Verica Pons-Moll, Gerard Univ Tubingen Tubingen Germany Max Planck Inst Informat Saarbrucken Germany Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781665445092

Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF [34], are designed to learn a single scene with a neural network and require dense multiview inputs. Testing on a new scene requires re-training from scratch, which takes 2-3 days. In this work, we introduce Stereo Radiance Fields (SRF), a neural view synthesis approach that is trained end-to-end, generalizes to new scenes, and requires only sparse views at test time. The core idea is a neural architecture inspired by classical multi-view stereo methods, which estimates surface points by finding similar image regions in stereo images. In SRF, we predict color and density for each 3D point given an encoding of its stereo correspondence in the input images. The encoding is implicitly learned by an ensemble of pair-wise similarities - emulating classical stereo. Experiments show that SRF learns structure instead of over-fitting on a scene. We train on multiple scenes of the DTU dataset and generalize to new ones without re-training, requiring only 10 sparse and spread-out views as input. We show that 10-15 minutes of fine-tuning further improve the results, achieving significantly sharper, more detailed results than scene-specific models.

关键词： Three-dimensional displays Image coding Image color analysis Computational modeling Pipelines Neural networks pattern recognition

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Depth-supervised NeRF: Fewer Views and Faster Training for Free

Depth-supervised NeRF: Fewer Views and Faster Training for F...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Deng, Kangle Liu, Andrew Zhu, Jun-Yan Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA Google Mountain View CA 94043 USA Argo AI Pittsburgh PA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect geometries when given an insufficient number of input views. One potential reason is that standard volumetric rendering does not enforce the constraint that most of a scene's geometry consist of empty space and opaque surfaces. We formalize the above assumption through DS-NeRF (Depth-supervised Neural Radiance Fields), a loss for learning radiance fields that takes advantage of readily-available depth supervision. We leverage the fact that current NeRF pipelines require images with known camera poses that are typically estimated by running structure-from-motion (SFM). Crucially, SFM also produces sparse 3D points that can be used as "free" depth supervision during training: we add a loss to encourage the distribution of a ray's terminating depth matches a given 3D keypoint, incorporating depth uncertainty. DS-NeRF can render better images given fewer training views while training 3x faster. Further, we show that our loss is compatible with other recently proposed NeRF methods, demonstrating that depth is a cheap and easily digestible supervisory signal. And finally, we find that DS-NeRF can support other types of depth supervision such as scanned depth sensors and RGB-D reconstruction outputs.

关键词： Training Geometry Three-dimensional displays Uncertainty Pipelines Rendering (computer graphics) Surface fitting

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BANMo: Building Animatable 3D Neural Models from Many Casual Videos

BANMo: Building Animatable 3D Neural Models from Many Casual...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yang, Gengshan Minh Vo Neverova, Natalia Ramanan, Deva Vedaldi, Andrea Joo, Hanbyul Meta AI Menlo Pk CA USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Meta Real Labs Burlingame CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Prior work for articulated 3D shape reconstruction often relies on specialized multi-view and depth sensors or pre-built deformable 3D models. Such methods do not scale to diverse sets of objects in the wild. We present a method that requires neither of them. It aims to create high-fidelity, articulated 3D models from many casual RGB videos in a differentiable rendering framework. Our key insight is to merge three schools of thought: (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) canonical embeddings that establish correspondences between pixels and a canonical 3D model, and (3) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization. We introduce neural blend skinning models that allow for differentiable and invertible articulated deformations. When combined with canonical embeddings, such models allow us to establish dense correspondences across videos that can be self-supervised with cycle consistency. On real and synthetic datasets, our method shows higher-fidelity 3D reconstructions than prior works for humans and animals, with the ability to render realistic images from novel viewpoints. Project page: https://***/.

关键词： Deformable models Solid modeling Three-dimensional displays Tracking Shape Pose estimation Rendering (computer graphics)

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SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance

SDD-FIQA: Unsupervised Face Image Quality Assessment with Si...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Fu-Zhao Ou Chen, Xingyu Zhang, Ruixin Huang, Yuge Li, Shaoxin Li, Jilin Li, Yong Cao, Liujuan Yuan-Gen Wang Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Peoples R China Tencent Youtu Lab Beijing Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China

ISBN: (纸本)9781665445092

In recent years, Face Image Quality Assessment (FIQA) has become an indispensable part of the face recognition system to guarantee the stability and reliability of recognition performance in an unconstrained scenario. For this purpose, the FIQA method should consider both the intrinsic property and the recognizability of the face image. Most previous works aim to estimate the sample-wise embedding uncertainty or pair-wise similarity as the quality score, which only considers the partial information from the intra-class. However, these methods ignore the valuable information from the inter-class, which is for estimating the recognizability of face image. In this work, we argue that a high-quality face image should be similar to its intra-class samples and dissimilar to its inter-class samples. Thus, we propose a novel unsupervised FIQA method that incorporates Similarity Distribution Distance for Face Image Quality Assessment (SDD-FIQA). Our method generates quality pseudo-labels by calculating the Wasserstein Distance (WD) between the intra-class and inter-class similarity distributions. With these quality pseudo-labels, we are capable of training a regression network for quality prediction. Extensive experiments on benchmark datasets demonstrate that the proposed SDD-FIQA surpasses the state-of-the-arts by an impressive margin. Meanwhile, our method shows good generalization across different recognition systems.

关键词： Image quality Training Measurement computer vision Image recognition Uncertainty Face recognition

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Reformulating HOI Detection as Adaptive Set Prediction

Reformulating HOI Detection as Adaptive Set Prediction

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Chen, Mingfei Liao, Yue Liu, Si Chen, Zhiyuan Wang, Fei Qian, Chen Huazhong Univ Sci & Technol Wuhan Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Determining which image regions to concentrate is critical for Human-Object Interaction (HOI) detection. Conventional HOI detectors focus on either detected human and object pairs or pre-defined interaction locations, which limits learning of the effective features. In this paper, we reformulate HOI detection as an adaptive set prediction problem, with this novel formulation, we propose an Adaptive Set-based one-stage framework (AS-Net) with parallel instance and interaction branches. To attain this, we map a trainable interaction query set to an interaction prediction set with transformer. Each query adaptively aggregates the interaction-relevant features from global contexts through multi-head co-attention. Besides, the training process is supervised adaptively by matching each ground-truth with the interaction prediction. Furthermore, we design an effective instance-aware attention module to introduce instructive features from the instance branch into the interaction branch. Our method outperforms previous state-of-the-art methods without any extra human pose and language features on three challenging HOI detection datasets. Especially, we achieve over 31% relative improvement on a large scale HICO-DET dataset. Code is available at https://***/yoyomimi/AS-Net.

关键词： Training computer vision Codes Aggregates Detectors Feature extraction Transformers

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From a Bird's Eye View to See: Joint Camera and Subject Registration without the Camera Calibration

From a Bird's Eye View to See: Joint Camera and Subject Regi...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Qian, Zekun Han, Ruize Feng, Wei Wang, Song Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Univ South Carolina Columbia SC 29208 USA

ISBN: (纸本)9798350353013;9798350353006

We tackle a new problem of multi-view camera and subject registration in the bird's eye view (BEV) without pre-given camera calibration, which promotes the multi-view subject registration problem to a new calibration-free stage. This greatly alleviates the limitation in many practical applications. However, this is a very challenging problem since its only input is several RGB images from different first-person views (FPVs), without the BEV image and the calibration of the FPVs, while the output is a unified plane aggregated from all views with the positions and orientations of both the subjects and cameras in a BEV. For this purpose, we propose an end-to-end framework solving camera and subject registration together by taking advantage of their mutual dependence, whose main idea is as below: i) creating a subject view-transform module (VTM) to project each pedestrian from FPV to a virtual BEV, ii) deriving a multi-view geometry-based spatial alignment module (SAM) to estimate the relative camera pose in a unified BEV, iii) selecting and refining the subject and camera registration results within the unified BEV. We collect a new large-scale synthetic dataset with rich annotations for training and evaluation. Additionally, we also collect a real dataset for cross-domain evaluation. The experimental results show the remarkable effectiveness of our method. The code and proposed datasets are available at BEVSee.

关键词： Camera Registration computer vision Multiview Detection Subject Registration

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Self-Aligned Video Deraining with Transmission-Depth Consistency

Self-Aligned Video Deraining with Transmission-Depth Consist...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Yan, Wending Tan, Robby T. Yang, Wenhan Dai, Dengxin Natl Univ Singapore Singapore Singapore Yale NUS Coll Singapore Singapore City Univ Hong Kong Hong Kong Peoples R China Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665445092

In this paper, we address the problem of rain streaks and rain accumulation removal in video, by developing a self-alignment network with transmission-depth consistency. Existing video based deraining methods focus only on rain streak removal, and commonly use optical flow to align the rain video frames. However, besides rain streaks, rain accummulation can considerably degrade visibility;and, optical flow estimation in a rain video is still erroneous, making the deraining performance tend to be inaccurate. Our method employs deformable convolution layers in our encoder to achieve feature-level frame alignment, and hence avoids using optical flow. For rain streaks, our method predicts the current frame from its adjacent frames, such that rain streaks that appear randomly in the temporal domain can be removed. For rain accumulation, our method employs a transmission-depth consistency loss to resolve the ambiguity between the depth and water-droplet density. Our network estimates the depth from consecutive rain-accumulation-removal outputs, and calculates the transmission map using a commonly used physics model. To ensure photometric-temporal and depth-temporal consistencies, our method estimates the camera poses, so that it can warp one frame to its adjacent frames. Experimental results show that our method is effective in removing both rain streaks and rain accumulation, outperforming those of state-of-the-art methods quantitatively and qualitatively.

关键词： Optical losses Visualization computer vision Rain Convolution Estimation Propagation losses

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SGTR: End-to-end Scene Graph Generation with Transformer

SGTR: End-to-end Scene Graph Generation with Transformer

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Li, Rongjie Zhang, Songyang He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, wc propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation.(1)

关键词： Visualization Image analysis Transformers pattern recognition Decoding Bipartite graph Proposals

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When to Prune? A Policy towards Early Structural Pruning

When to Prune? A Policy towards Early Structural Pruning

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shen, Maying Molchanov, Pavlo Yin, Hongxu Alvarez, Jose M. NVIDIA Santa Clara CA 95051 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Pruning enables appealing reductions in network memory footprint and time complexity. Conventional post-training pruning techniques lean towards efficient inference while overlooking the heavy computation for training. Recent exploration of pre-training pruning at initialization hints on training cost reduction via pruning, but suffers noticeable performance degradation. We attempt to combine the benefits of both directions and propose a policy that prunes as early as possible during training without hurting performance. Instead of pruning at initialization, our method exploits initial dense training for few epochs to quickly guide the architecture, while constantly evaluating dominant sub-networks via neuron importance ranking. This unveils dominant sub-networks whose structures turn stable, allowing conventional pruning to be pushed earlier into the training. To do this early, we further introduce an Early Pruning Indicator (EPI) that relies on sub-network architectural similarity and quickly triggers pruning when the sub-network's architecture stabilizes. Through extensive experiments on ImageNet, we show that EPI empowers a quick tracking of early training epochs suitable for pruning, offering same efficacy as an otherwise "oracle" grid-search that scans through epochs and requires orders of magnitude more compute. Our method yields 1.4% top-1 accuracy boost over state-of-the-art pruning counterparts, cuts down training cost on GPU by 2.4x, hence offers a new efficiency-accuracy boundary for network pruning during training.

关键词： Training Measurement Costs Neurons computer architecture Object detection Power system stability

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