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MuIT: An End-to-End Multitask Learning Transformer

MuIT: An End-to-End Multitask Learning Transformer

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Bhattacharjee, Deblina Zhang, Tong Susstrunk, Sabine Salzmann, Mathieu School of Computer and Communication Sciences Epfl Switzerland

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmentation, reshading, surface normal estimation, 2D keypoint detection, and edge detection. Based on the Swin transformer model, our framework encodes the input image into a shared representation and makes predictions for each vision task using task-specific transformer-based decoder heads. At the heart of our approach is a shared attention mechanism modeling the dependencies across the tasks. We evaluate our model on several multitask benchmarks, showing that our MulT framework outperforms both the state-of-the art multitask convolutional neural network models and all the respective single task transformer models. Our experiments further highlight the benefits of sharing attention across all the tasks, and demonstrate that our MulT model is robust and generalizes well to new domains. Our project website is at https://***/MulT/. © 2022 ieee.

关键词： Heart Image segmentation Computational modeling Image edge detection Semantics Estimation Predictive models

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HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model

HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation ...

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

作者： Junayed, Masum Shah Sadeghzadeh, Arezoo Islam, Md Baharul Wong, Lai-Kuan Aydin, Tarkan Bahcesehir Univ Istanbul Turkey Amer Univ Malta Cospicua Malta Multimedia Univ Cyberjaya Malaysia

ISBN: (纸本)9781665487399

Monocular omnidirectional depth estimation is receiving considerable research attention due to its broad applications for sensing 360 degrees surroundings. Existing approaches in this field suffer from limitations in recovering small object details and data lost during the ground-truth depth map acquisition. In this paper, a novel monocular omnidirectional depth estimation model, namely HiMODE is proposed based on a hybrid CNN+Transformer (encoder-decoder) architecture whose modules are efficiently designed to mitigate distortion and computational cost, without performance degradation. Firstly, we design a feature pyramid network based on the HNet block to extract high-resolution features near the edges. The performance is further improved, benefiting from a self and cross attention layer and spatial/temporal patches in the Transformer encoder and decoder, respectively. Besides, a spatial residual block is employed to reduce the number of parameters. By jointly passing the deep features extracted from an input image at each backbone block, along with the raw depth maps predicted by the transformer encoder-decoder, through a context adjustment layer, our model can produce resulting depth maps with better visual quality than the ground-truth. Comprehensive ablation studies demonstrate the significance of each individual module. Extensive experiments conducted on three datasets;Stanford3D, Matterport3D, and SunCG, demonstrate that HiMODE can achieve state-of-the-art performance for 360 degrees monocular depth estimation. Complete project code and supplementary materials are available at https://***/himode5008/HiMODE.

关键词： Visualization Three-dimensional displays Estimation Lighting computer architecture Feature extraction Transformers

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A Challenging Benchmark of Anime Style recognition

A Challenging Benchmark of Anime Style Recognition

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

作者： Li, Haotang Guo, Shengtao Lyu, Kailin Yang, Xiao Chen, Tianchen Zhu, Jianqing Zeng, Huanqiang Huaqiao Univ Coll Engn Quanzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Given two images of different anime roles, anime style recognition (ASR) aims to learn abstract painting style to determine whether the two images are from the same work, which is an interesting but challenging problem. Unlike biometric recognition, such as face recognition, iris recognition, and person re-identification, ASR suffers from a much larger semantic gap but receives less attention. In this paper, we propose a challenging ASR benchmark. Firstly, we collect a large-scale ASR dataset (LSASRD), which contains 20,937 images of 190 anime works and each work at least has ten different roles. In addition to the large-scale, LSASRD contains a list of challenging factors, such as complex illuminations, various poses, theatrical colors and exaggerated compositions. Secondly, we design a cross-role protocol to evaluate ASR performance, in which query and gallery images must come from different roles to validate an ASR model is to learn abstract painting style rather than learn discriminative features of roles. Finally, we apply two powerful person re-identification methods, namely, AGW and TransReID, to construct the baseline performance on LSASRD. Surprisingly, the recent transformer model (i.e., TransReID) only acquires a 42.24% mAP on LSASRD. Therefore, we believe that the ASR task of a huge semantic gap deserves deep and long-term research. We will open our dataset and code at https://***/nkjcqvcpi/ASR.

关键词： Protocols Face recognition Semantics Benchmark testing Metadata Feature extraction Transformers

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Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation

Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mes...

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

作者： Moon, Gyeongsik Choi, Hongsuk Lee, Kyoung Mu Seoul Natl Univ Dept ECE & ASRI Seoul South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Whole-body 3D human mesh estimation aims to reconstruct the 3D human body, hands, and face simultaneously. Although several methods have been proposed, accurate prediction of 3D hands, which consist of 3D wrist and fingers, still remains challenging due to two reasons. First, the human kinematic chain has not been carefully considered when predicting the 3D wrists. Second, previous works utilize body features for the 3D fingers, where the body feature barely contains finger information. To resolve the limitations, we present Hand4Whole, which has two strong points over previous works. First, we design Pose2Pose, a module that utilizes joint features for 3D joint rotations. Using Pose2Pose, Hand4Whole utilizes hand MCP joint features to predict 3D wrists as MCP joints largely contribute to 3D wrist rotations in the human kinematic chain. Second, Hand4Whole discards the body feature when predicting 3D finger rotations. Our Hand4Whole is trained in an end-to-end manner and produces much better 3D hand results than previous whole-body 3D human mesh estimation methods. The codes are available here (1).

关键词： Wrist computer vision Three-dimensional displays Codes Face recognition conferences Pose estimation

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Investigating Neural Architectures by Synthetic Dataset Design

Investigating Neural Architectures by Synthetic Dataset Desi...

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

作者： Courtois, Adrien Morel, Jean-Michel Arias, Pablo ENS Paris Saclay Ctr Borelli 4 Av Sci F-91190 Gif Sur Yvette France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Recent years have seen the emergence of many new neural network structures (architectures and layers). To solve a given task, a network requires a certain set of abilities reflected in its structure. The required abilities depend on each task. There is so far no systematic study of the real capacities of the proposed neural structures. The question of what each structure can and cannot achieve is only partially answered by its performance on common benchmarks. Indeed, natural data contain complex unknown statistical cues. It is therefore impossible to know what cues a given neural structure is taking advantage of in such data. In this work, we sketch a methodology to measure the effect of each structure on a network's ability, by designing ad hoc synthetic datasets. Each dataset is tailored to assess a given ability and is reduced to its simplest form: each input contains exactly the amount of information needed to solve the task. We illustrate our methodology by building three datasets to evaluate each of the three following network properties: a) the ability to link local cues to distant inferences, b) the translation covariance and c) the ability to group pixels with the same characteristics and share information among them. Using a first simplified depth estimation dataset, we pinpoint a serious nonlocal deficit of the U-Net. We then evaluate how to resolve this limitation by embedding its structure with nonlocal layers, which allow computing complex features with long-range dependencies. Using a second dataset, we compare different positional encoding methods and use the results to further improve the U-Net on the depth estimation task. The third introduced dataset serves to demonstrate the need for self-attention-like mechanisms for resolving more realistic depth estimation tasks.

关键词： computer vision Systematics conferences Neural networks Buildings Estimation computer architecture

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Characterizing Target-absent Human Attention

Characterizing Target-absent Human Attention

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

作者： Chen, Yupei Yang, Zhibo Chakraborty, Souradeep Mondal, Sounak Ahn, Seoyoung Samaras, Dimitris Hoai, Minh Zelinsky, Gregory Smith Kettlewell Eye Res Inst 2232 Webster St San Francisco CA 94115 USA SUNY Stony Brook Stony Brook NY 11794 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human efficiency in finding a target in an image has attracted the attention of machine learning researchers, but what about when no target is there? Knowing how people search in the absence of a target, and when they stop, is important for Human-computer-interaction systems attempting to predict human gaze behavior in the wild. Here we report a rigorous evaluation of target-absent search behavior using the COCO-Search18 dataset to train state-of-the-art models. We focus on two specific aims. First, we characterize the presence of a target guidance signal in target-absent search behavior by comparing it to target-present guidance and free viewing. We do this by comparing how well a model trained on one type of fixation behavior (target-present, target-absent, free viewing) can predict behavior in either the same or different task. To compare target-absent search to free viewing behavior we created COCO-FreeView, a dataset of free-viewing fixations for the same images used in COCO-Search18. These comparisons revealed the existence of a target guidance signal in target-absent search, albeit one much less dominant compared to when a target actually appeared in an image, and that the target-absent guidance signal was similar to free viewing in that saliency and center bias were both weighted more than guidance from target features. Our second aim focused on the stopping criteria, a question intrinsic to target-absent search. Here we propose to train a foveated target detector whose target detection representation is sensitive to the relationship between distance from the fovea. Then combining the predicted target detection representation with other information such as fixation history and subject ID, our model outperforms the baselines in predicting when a person stops moving his attention during target-absent search.

关键词： computer vision conferences Object detection Machine learning Detectors Predictive models Behavioral sciences

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Interactiveness Field in Human-Object Interactions

Interactiveness Field in Human-Object Interactions

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2022 ieee/CVF conference on computer vision and pattern recognition, cvpr 2022

作者： Liu, Xinpeng Li, Yong-Lu Wu, Xiaoqian Tai, Yu-Wing Lu, Cewu Tang, Chi-Keung Shanghai Jiao Tong University China HKUST Hong Kong Kuaishou Technology China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Human-Object Interaction (HOI) detection plays a core role in activity understanding. Though recent two/one-stage methods have achieved impressive results, as an essential step, discovering interactive human-object pairs remains challenging. Both one/two-stage methods fail to effectively extract interactive pairs instead of generating redundant negative pairs. In this work, we introduce a previously overlooked interactiveness bimodal prior: given an object in an image, after pairing it with the humans, the generated pairs are either mostly non-interactive, or mostly interactive, with the former more frequent than the latter. Based on this interactiveness bimodal prior we propose the 'interactiveness field'. To make the learned field compatible with real HOI image considerations, we propose new energy constraints based on the cardinality and difference in the inherent 'interactiveness field' underlying interactive versus non-interactive pairs. Consequently, our method can detect more precise pairs and thus significantly boost HOI detection performance, which is validated on widely-used benchmarks where we achieve decent improvements over state-of-the-arts. Our code is available at https://***-Field. © 2022 ieee.

关键词： computer vision Codes Benchmark testing pattern recognition

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DA-AE: Disparity-Alleviation Auto-Encoder Towards Categorization of Heritage Images for Aggrandized 3D Reconstruction.

DA-AE: Disparity-Alleviation Auto-Encoder Towards Categoriza...

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

作者： Hegde, Dikshit Anvekar, Tejas Tabib, Ramesh Ashok Mudengudi, Uma KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnakata India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

In this paper, we propose DA-AE: Disparity Alleviation AutoEncoder for categorization of heritage images towards 3D reconstruction. Recent survey on preservation of heritage shows demand for the digitization and conservations of heritage sites owing to their susceptibility to natural disasters and human acts. Digital conservation can be facilitated via crowdsourcing of data useful for construction of 3D models. Data from multiple sites sourced may result in elimination of relevant images due to the limitations of the pipeline. Curation and categorization of the crowdsourced data enables better 3D reconstruction. 3D reconstruction pipelines demand correlation between the data and also tries to eliminate the irrelevant information. The reconstruction pipeline is sensitive to selection of initial pair for reconstruction. By categorising individual sites, crowdsourced data can be used to create better 3D reconstructed models. Categorization of crowdsourced data demands learning robust representations of data. Towards this, we propose DA-AE for improved representation and categorization of data in latent space, along with a disparity alleviation loss. We demonstrate categorization as an event, with clustering as a downstream task. We compare our results of clustering with state-of-the-art methods on benchmark datasets (MNIST, FashionMNIST, and USPS). We demonstrate the effects of our categorization using custom dataset IDH10 and compare the results with state-ofthe-art methods. We show a systematic and qualitative influence of the proposed method on 3D reconstruction of data. [GRAPHICS] .

关键词： Solid modeling Three-dimensional displays Systematics Pipelines Benchmark testing Data models pattern recognition

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MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

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

作者： Nair, Saeejith Abbasi, Saad Wong, Alexander Shafiee, Mohammad Javad Univ Waterloo Waterloo ON Canada DarwinAI Waterloo ON Canada

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Neural Architecture Search (NAS) has enabled automatic discovery of more efficient neural network architectures, especially for mobile and embedded vision applications. Although recent research has proposed ways of quickly estimating latency on unseen hardware devices with just a few samples, little focus has been given to the challenges of estimating latency on runtimes using optimized graphs, such as TensorRT and specifically for edge devices. As devices like NVIDIA's Jetsons get more popular in embedded computing and robotics, we observe a pressing need to more accurately estimate inference latency of neural network architectures on diverse runtimes, including highly optimized ones. In this work, we propose MAPLE-Edge, an edge device-oriented extension of MAPLE, the state-of-the-art latency predictor for general purpose hardware, where we train a regression network on architecture-latency pairs in conjunction with a hardware-runtime descriptor to effectively estimate latency on a diverse pool of edge devices. Compared to MAPLE, MAPLE-Edge can describe the runtime and target device platform using a much smaller set of CPU performance counters that are widely available on all Linux kernels, while still achieving up to +49.6% accuracy gains against previous state-of-the-art baseline methods on optimized edge device runtimes, using just 10 measurements from an unseen target device. We also demonstrate that unlike MAPLE which performs best when trained on a pool of devices sharing a common runtime, MAPLE-Edge can effectively generalize across runtimes by applying a trick of normalizing performance counters by the operator latency, in the measured hardware-runtime descriptor. Lastly, we show that for runtimes exhibiting lower than desired accuracy, performance can be boosted by collecting additional samples from the target device, with an extra 90 samples translating to gains of nearly +40%.

关键词： Performance evaluation Training Runtime Linux Neural networks computer architecture Pressing

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SwiniPASSR: Swin Transformer based Parallax Attention Network for Stereo Image Super-Resolution

SwiniPASSR: Swin Transformer based Parallax Attention Networ...

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

作者： Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Guo, Guodong Bigo Technol Pte Ltd Beijing Peoples R China Beijing Univ Posts & Telecommun Smart Med Innovat Lab Beijing Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China Baidu Res Inst Deep Learning Beijing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

With binocular cameras being widely accepted, the study of stereo image super resolution (Stereo SR) has received increasing attention. Different from single image super resolution (SISR) setting, it is more challenging for utilizing both intra-view and cross-view information. Although prior convolution-based works have achieved admirable progress, few attempts have explored the possibility of the Transformer-based architecture for stereo image SR, which has demonstrated promising performance in several visual tasks. In this paper, we propose a novel approach namely SwiniPASSR, which adopts Swin Transformer as the backbone, meanwhile incorporating it with the Bi-directional Parallax Attention Module (biPAM) to maximize auxiliary information given by the binocular mechanism. Even Transformer and parallax attention mechanism (PAM) have been separately proved usefulness by prior studies, we find that simply integrating convolution-based PAM with Transformer or directly optimizing for stereo SR problem was may not achieve desirable result. We therefore introduced a conversion layer to resolve integration and adopted progressive training strategy to learn disparity correspondence through progressively enlarged receptive fields. Both extensive experiments and ablation studies demonstrate the effectiveness of our proposed SwiniPASSR. In particular, in the NTIRE 2022: Stereo Image Super-Resolution Challenge, we report 23.71dB PSNR and 0.7295 SSIM performance which ranked 2nd place on the leaderboard.

关键词： Training Convolutional codes Visualization Superresolution Estimation Transformers pattern recognition

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