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Phone2Proc: Bringing Robust Robots Into Our Chaotic World

Phone2Proc: Bringing Robust Robots Into Our Chaotic World

引用

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

作者： Deitke, Matt Hendrix, Rose Farhadi, Ali Ehsani, Kiana Kembhavi, Aniruddha Allen Inst AI PRIOR Seattle WA 98103 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9798350301298

Training embodied agents in simulation has become mainstream for the embodied AI community. However, these agents often struggle when deployed in the physical world due to their inability to generalize to real-world environments. In this paper, we present Phone2Proc, a method that uses a 10-minute phone scan and conditional procedural generation to create a distribution of training scenes that are semantically similar to the target environment. The generated scenes are conditioned on the wall layout and arrangement of large objects from the scan, while also sampling lighting, clutter, surface textures, and instances of smaller objects with randomized placement and materials. Leveraging just a simple RGB camera, training with Phone2Proc shows massive improvements from 34.7% to 70.7% success rate in sim-to-real ObjectNav performance across a test suite of over 200 trials in diverse real-world environments, including homes, offices, and RoboTHOR. Furthermore, Phone2Proc's diverse distribution of generated scenes makes agents remarkably robust to changes in the real world, such as human movement, object rearrangement, lighting changes, or clutter.

关键词： Embodied vision: Active agents simulation

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SViTT: Temporal Learning of Sparse Video-Text Transformers

SViTT: Temporal Learning of Sparse Video-Text Transformers

引用

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

作者： Li, Yi Min, Kyle Tripathi, Subarna Vasconcelos, Nuno Univ Calif San Diego La Jolla CA 92093 USA Intel Labs Santa Clara CA USA

ISBN: (纸本)9798350301298

Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size;the curse of dimensionality for multi-frame modeling;and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://***/projects/svitt.

关键词： and reasoning language vision

来源：评论

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Efficient and Explicit Modelling of Image Hierarchies for Image Restoration

Efficient and Explicit Modelling of Image Hierarchies for Im...

引用

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

作者： Li, Yawei Fan, Yuchen Xiang, Xiaoyu Demandolx, Denis Ranjan, Rakesh Timofte, Radu Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Meta Real Labs Menlo Pk CA 33137 USA Univ Wurzburg Wurzburg Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (纸本)9798350301298

The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration. To achieve that, we start by analyzing two important properties of natural images including cross-scale similarity and anisotropic image features. Inspired by that, we propose the anchored stripe self-attention which achieves a good balance between the space and time complexity of self-attention and the modelling capacity beyond the regional range. Then we propose a new network architecture dubbed GRL to explicitly model image hierarchies in the Global, Regional, and Local range via anchored stripe self-attention, window self-attention, and channel attention enhanced convolution. Finally, the proposed network is applied to 7 image restoration types, covering both real and synthetic settings. The proposed method sets the new state-of-the-art for several of those. Code will be available at https://***/ofsoundof/***.

关键词： Low-level vision

来源：评论

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ELSA: Exploiting Layer-wise N:M Sparsity for vision Transformer Acceleration

ELSA: Exploiting Layer-wise N:M Sparsity for Vision Transfor...

引用

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

作者： Huang, Ning-Chi Chang, Chi-Chih Lin, Wei-Cheng Taka, Endri Marculescu, Diana Wu, Kai-Chiang Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Univ Texas Austin Austin TX USA

ISBN: (纸本)9798350365474

N:M sparsity is an emerging model compression method supported by more and more accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing N:M sparsity methods compress neural networks with a uniform setting for all layers in a network or heuristically determine the layer-wise configuration by considering the number of parameters in each layer. However, very few methods have been designed for obtaining a layer-wise customized N:M sparse configuration for vision transformers (ViTs), which usually consist of transformer blocks involving the same number of parameters. In this work, to address the challenge of selecting suitable sparse configuration for ViTs on N:M sparsity-supporting accelerators, we propose ELSA, Exploiting Layer-wise N:M Sparsity for ViTs. Considering not only all N:M sparsity levels supported by a given accelerator but also the expected throughput improvement, our methodology can reap the benefits of accelerators supporting mixed sparsity by trading off negligible accuracy loss with both memory usage and inference time reduction for ViT models. For instance, our approach achieves a noteworthy 2.9x reduction in FLOPs to both Swin-B and DeiT-B with only a marginal degradation of accuracy on ImageNet. Our code is publicly available at https://***/ningchihuang/ ELSA.

关键词： Deep neural networks

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Patch-Craft Self-Supervised Training for Correlated Image Denoising

Patch-Craft Self-Supervised Training for Correlated Image De...

引用

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

作者： Vaksman, Gregory Elad, Michael Technion CS Dept Haifa Israel

ISBN: (纸本)9798350301298

Supervised neural networks are known to achieve excellent results in various image restoration tasks. However, such training requires datasets composed of pairs of corrupted images and their corresponding ground truth targets. Unfortunately, such data is not available in many applications. For the task of image denoising in which the noise statistics is unknown, several self-supervised training methods have been proposed for overcoming this difficulty. Some of these require knowledge of the noise model, while others assume that the contaminating noise is uncorrelated, both assumptions are too limiting for many practical needs. This work proposes a novel self-supervised training technique suitable for the removal of unknown correlated noise. The proposed approach neither requires knowledge of the noise model nor access to ground truth targets. The input to our algorithm consists of easily captured bursts of noisy shots. Our algorithm constructs artificial patch-craft images from these bursts by patch matching and stitching, and the obtained crafted images are used as targets for the training. Our method does not require registration of the images within the burst. We evaluate the proposed framework through extensive experiments with synthetic and real image noise.

关键词： Low-level vision

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Metadata-Based RAW Reconstruction via Implicit Neural Functions

Metadata-Based RAW Reconstruction via Implicit Neural Functi...

引用

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

作者： Li, Leyi Qiao, Huijie Ye, Qi Yang, Qinmin Zhejiang Univ Hangzhou Peoples R China Chinese Acad Sci Beijing Peoples R China Key Lab CS Hangzhou Peoples R China AUS Zhejiang Prov Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Many low-level computer vision tasks are desirable to utilize the unprocessed RAW image as input, which remains the linear relationship between pixel values and scene radiance. Recent works advocate to embed the RAW image samples into sRGB images at capture time, and reconstruct the RAW from sRGB by these metadata when needed. However, there still exist some limitations in making full use of the metadata. In this paper, instead of following the perspective of sRGB-to-RAW mapping, we reformulate the problem as mapping the 2D coordinates of the metadata to its RAW values conditioned on the corresponding sRGB values. With this novel formulation, we propose to reconstruct the RAW image with an implicit neural function, which achieves significant performance improvement (more than 10dB average PSNR) only with the uniform sampling. Compared with most deep learning-based approaches, our method is trained in a self-supervised way that requiring no pre-training on different camera ISPs. We perform further experiments to demonstrate the effectiveness of our method, and show that our framework is also suitable for the task of guided super-resolution.

关键词： Low-level vision

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Feature Aggregated Queries for Transformer-based Video Object Detectors

Feature Aggregated Queries for Transformer-based Video Objec...

引用

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

作者： Cui, Yiming Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9798350301298

Video object detection needs to solve feature degradation situations that rarely happen in the image domain. One solution is to use the temporal information and fuse the features from the neighboring frames. With Transformer-based object detectors getting a better performance on the image domain tasks, recent works began to extend those methods to video object detection. However, those existing Transformer-based video object detectors still follow the same pipeline as those used for classical object detectors, like enhancing the object feature representations by aggregation. In this work, we take a different perspective on video object detection. In detail, we improve the qualities of queries for the Transformer-based models by aggregation. To achieve this goal, we first propose a vanilla query aggregation module that weighted averages the queries according to the features of the neighboring frames. Then, we extend the vanilla module to a more practical version, which generates and aggregates queries according to the features of the input frames. Extensive experimental results validate the effectiveness of our proposed methods: On the challenging ImageNet VID benchmark, when integrated with our proposed modules, the current state-of-the-art Transformer-based object detectors can be improved by more than 2.4% on mAP and 4.2% on AP(50). Code is available at https://***/YimingCuiCuiCui/FAQ.

关键词： detection recognition: Categorization retrieval

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Transformer-based Unified recognition of Two Hands Manipulating Objects

Transformer-based Unified Recognition of Two Hands Manipulat...

引用

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

作者： Cho, Hoseong Kim, Chanwoo Kim, Jihyeon Lee, Seongyeong Ismayilzada, Elkhan Baek, Seungryul UNIST Ulsan South Korea

ISBN: (纸本)9798350301298

Understanding the hand-object interactions from an egocentric video has received a great attention recently. So far, most approaches are based on the convolutional neural network (CNN) features combined with the temporal encoding via the long short-term memory (LSTM) or graph convolution network (GCN) to provide the unified understanding of two hands, an object and their interactions. In this paper, we propose the Transformer-based unified framework that provides better understanding of two hands manipulating objects. In our framework, we insert the whole image depicting two hands, an object and their interactions as input and jointly estimate 3 information from each frame: poses of two hands, pose of an object and object types. Afterwards, the action class defined by the hand-object interactions is predicted from the entire video based on the estimated information combined with the contact map that encodes the interaction between two hands and an object. Experiments are conducted on H2O and FPHA benchmark datasets and we demonstrated the superiority of our method achieving the state-of-the-art accuracy. Ablative studies further demonstrate the effectiveness of each proposed module.

关键词： body gesture Humans: Face movement pose

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Knowledge Distillation for Efficient Instance Semantic Segmentation with Transformers

Knowledge Distillation for Efficient Instance Semantic Segme...

引用

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

作者： Li, Maohui Halstead, Michael McCool, Chris Univ Bonn Bonn Germany Lamarr Inst Machine Learning & Artificial Intelli Dortmund Germany

ISBN: (纸本)9798350365474

Instance-based semantic segmentation provides detailed per-pixel scene understanding information crucial for both computer vision and robotics applications. However, state-of-the-art approaches such as Mask2Former are computationally expensive and reducing this computational burden while maintaining high accuracy remains challenging. Knowledge distillation has been regarded as a potential way to compress neural networks, but to date limited work has explored how to apply this to distill information from the output queries of a model such as Mask2Former. In this paper, we match the output queries of the student and teacher models to enable a query-based knowledge distillation scheme. We independently match the teacher and the student to the groundtruth and use this to define the teacher to student relationship for knowledge distillation. Using this approach we show that it is possible to perform knowledge distillation where the student models can have a lower number of queries and the backbone can be changed from a Transformer architecture to a convolutional neural network architecture. Experiments on two challenging agricultural datasets, sweet pepper (BUP20) and sugar beet (SB20), and Cityscapes demonstrate the efficacy of our approach. Across the three datasets the student models obtain an average absolute performance improvement in AP of 1.8 and 1.9 points for ResNet-50 and Swin-Tiny backbone respectively. To the best of our knowledge, this is the first work to propose knowledge distillation schemes for instance semantic segmentation with transformer-based models.

关键词： computer vision for Agriculture Automation Knowledge Distillation Efficient Instance Segmentation Transformercomputer vision for Agriculture Automation Knowledge Distillation Efficient Instance Segmentation Transformer

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Quality-aware Pre-trained Models for Blind Image Quality Assessment

Quality-aware Pre-trained Models for Blind Image Quality Ass...

引用

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

作者： Zhao, Kai Yuan, Kun Sun, Ming Li, Mading Wen, Xing Kuaishou Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

Blind image quality assessment (BIQA) aims to automatically evaluate the perceived quality of a single image, whose performance has been improved by deep learning-based methods in recent years. However, the paucity of labeled data somewhat restrains deep learning-based BIQA methods from unleashing their full potential. In this paper, we propose to solve the problem by a pretext task customized for BIQA in a self-supervised learning manner, which enables learning representations from orders of magnitude more data. To constrain the learning process, we propose a quality-aware contrastive loss based on a simple assumption: the quality of patches from a distorted image should be similar, but vary from patches from the same image with different degradations and patches from different images. Further, we improve the existing degradation process and form a degradation space with the size of roughly 2 x 10(7). After pre-trained on ImageNet using our method, models are more sensitive to image quality and perform significantly better on downstream BIQA tasks. Experimental results show that our method obtains remarkable improvements on popular BIQA datasets.

关键词： Low-level vision

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