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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

来源：评论

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Zero-Shot Dual-Lens Super-Resolution

Zero-Shot Dual-Lens Super-Resolution

引用

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

作者： Xu, Ruikang Yao, Mingde Xiong, Zhiwei Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350301298

The asymmetric dual-lens configuration is commonly available on mobile devices nowadays, which naturally stores a pair of wide-angle and telephoto images of the same scene to support realistic super-resolution (SR). Even on the same device, however, the degradation for modeling realistic SR is image-specific due to the unknown acquisition process (e.g., tiny camera motion). In this paper, we propose a zero-shot solution for dual-lens SR (ZeDuSR), where only the dual-lens pair at test time is used to learn an image-specific SR model. As such, ZeDuSR adapts itself to the current scene without using external training data, and thus gets rid of generalization difficulty. However, there are two major challenges to achieving this goal: 1) dual-lens alignment while keeping the realistic degradation, and 2) effective usage of highly limited training data. To overcome these two challenges, we propose a degradation-invariant alignment method and a degradation-aware training strategy to fully exploit the information within a single dual-lens pair. Extensive experiments validate the superiority of ZeDuSR over existing solutions on both synthesized and realworld dual-lens datasets. The implementation code is available at https://***/XrKang/ZeDuSR.

关键词： Low-level vision

来源：评论

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DisWOT: Student Architecture Search for Distillation WithOut Training

DisWOT: Student Architecture Search for Distillation WithOut...

引用

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

作者： Dong, Peijie Li, Lujun Wei, Zimian Natl Univ Def Technol Changsha Peoples R China Chinese Acad Sci Beijing 100864 Peoples R China

ISBN: (纸本)9798350301298

Knowledge distillation (KD) is an effective training strategy to improve the lightweight student models under the guidance of cumbersome teachers. However, the large architecture difference across the teacher-student pairs limits the distillation gains. In contrast to previous adaptive distillation methods to reduce the teacher-student gap, we explore a novel training-free framework to search for the best student architectures for a given teacher. Our work first empirically show that the optimal model under vanilla training cannot be the winner in distillation. Secondly, we find that the similarity of feature semantics and sample relations between random-initialized teacher-student networks have good correlations with final distillation performances. Thus, we efficiently measure similarity matrixs conditioned on the semantic activation maps to select the optimal student via an evolutionary algorithm without any training. In this way, our student architecture search for Distillation WithOut Training (DisWOT) significantly improves the performance of the model in the distillation stage with at least 180x training acceleration. Additionally, we extend similarity metrics in DisWOT as new distillers and KD-based zero-proxies. Our experiments on CIFAR, ImageNet and NAS-Bench-201 demonstrate that our technique achieves state-of-the-art results on different search spaces. Our project and code are available at https://***/DisWOT-cvpr2023/.

关键词： Efficient and scalable vision

来源：评论

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vision Transformers are Parameter-Efficient Audio-Visual Learners

Vision Transformers are Parameter-Efficient Audio-Visual Lea...

引用

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

作者： Lin, Yan-Bo Sung, Yi-Lin Lei, Jie Bansal, Mohit Bertasius, Gedas UNC Chapel Hill Dept Comp Sci Chapel Hill NC 27514 USA

ISBN: (纸本)9798350301298

vision transformers (ViTs) have achieved impressive results on various computer vision tasks in the last several years. In this work, we study the capability of frozen ViTs, pretrained only on visual data, to generalize to audio-visual data without finetuning any of its original parameters. To do so, we propose a latent audio-visual hybrid (LAVISH) adapter that adapts pretrained ViTs to audio-visual tasks by injecting a small number of trainable parameters into every layer of a frozen ViT. To efficiently fuse visual and audio cues, our LAVISH adapter uses a small set of latent tokens, which form an attention bottleneck, thus, eliminating the quadratic cost of standard cross-attention. Compared to the existing modality-specific audio-visual methods, our approach achieves competitive or even better performance on various audio-visual tasks while using fewer tunable parameters and without relying on costly audio pretraining or external audio encoders. Our code is available at https://***/project_page/LAVISH/

关键词： Multi-modal learning

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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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Adaptive Human Matting for Dynamic Videos

Adaptive Human Matting for Dynamic Videos

引用

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

作者： Lin, Chung-Ching Wang, Jiang Luo, Kun Lin, Kevin Li, Linjie Wang, Lijuan Liu, Zicheng Microsoft Redmond WA 98052 USA

ISBN: (纸本)9798350301298

The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://***/microsoft/AdaM.

关键词： vision applications and systems

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DoNet: Deep De-overlapping Network for Cytology Instance Segmentation

DoNet: Deep De-overlapping Network for Cytology Instance Seg...

引用

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

作者： Jiang, Hao Zhang, Rushan Zhou, Yanning Wang, Yumeng Chen, Hao Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tencent AI Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798350301298

Cell instance segmentation in cytology images has significant importance for biology analysis and cancer screening, while remains challenging due to 1) the extensive overlapping translucent cell clusters that cause the ambiguous boundaries, and 2) the confusion of mimics and debris as nuclei. In this work, we proposed a De-overlapping Network (DoNet) in a decompose-and-recombined strategy. A Dual-path Region Segmentation Module (DRM) explicitly decomposes the cell clusters into intersection and complement regions, followed by a Semantic Consistency-guided Recombination Module (CRM) for integration. To further introduce the containment relationship of the nucleus in the cytoplasm, we design a Mask-guided Region Proposal Strategy (MRP) that integrates the cell attention maps for inner-cell instance prediction. We validate the proposed approach on ISBI2014 and CPS datasets. Experiments show that our proposed DoNet significantly outperforms other state-of-the-art (SOTA) cell instance segmentation methods. The code is available at https://***/DeepDoNet/DoNet.

关键词： cell microscopy Medical and biological vision

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Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models

Diffusion Art or Digital Forgery? Investigating Data Replica...

引用

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

作者： Somepalli, Gowthami Singla, Vasu Goldblum, Micah Geiping, Jonas Goldstein, Tom Univ Maryland College Pk MD 20742 USA NYU New York NY USA

ISBN: (纸本)9798350301298

Cutting-edge diffusion models produce images with high quality and customizability, enabling them to be used for commercial art and graphic design purposes. But do diffusion models create unique works of art, or are they replicating content directly from their training sets? In this work, we study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication. We also identify cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data. Project page: https://***/***

关键词： accountability ethics in vision fairness privacy Transparency

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Multiplicative Fourier Level of Detail

Multiplicative Fourier Level of Detail

引用

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

作者： Dou, Yishun Zheng, Zhong Jin, Qiaoqiao Ni, Bingbing Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Huawei Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We develop a simple yet surprisingly effective implicit representing scheme called Multiplicative Fourier Level of Detail (MFLOD) motivated by the recent success of multiplicative filter network. Built on multi-resolution feature grid/volume (e.g., the sparse voxel octree), each level's feature is first modulated by a sinusoidal function and then element-wisely multiplied by a linear transformation of previous layer's representation in a layer-to-layer recursive manner, yielding the scale-aggregated encodings for a subsequent simple linear forward to get final output. In contrast to previous hybrid representations relying on interleaved multilevel fusion and nonlinear activation-based decoding, MFLOD could be elegantly characterized as a linear combination of sine basis functions with varying amplitude, frequency, and phase upon the learned multilevel features, thus offering great feasibility in Fourier analysis. Comprehensive experimental results on implicit neural representation learning tasks including image fitting, 3D shape representation, and neural radiance fields well demonstrate the superior quality and generalizability achieved by the proposed MFLOD scheme.

关键词： vision + graphics

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Learning Expressive Prompting With Residuals for vision Transformers

Learning Expressive Prompting With Residuals for Vision Tran...

引用

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

作者： Das, Rajshekhar Dukler, Yonatan Ravichandran, Avinash Swarninathan, Ashwin Carnegie Mellon Univ Pittsburgh PA 15213 USA AWS AI Labs Shanghai Peoples R China

ISBN: (纸本)9798350301298

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of parameters into the input and intermediate representations of a pre-trained model. In this work, we present Expressive Prompts with Residuals (EXPRES) which modifies the prompt learning paradigm specifically for effective adaptation of vision transformers (ViT). Our method constructs downstream representations via learnable "output" tokens (shallow prompts), that are akin to the learned class tokens of the ViT. Further for better steering of the downstream representation processed by the frozen transformer, we introduce residual learnable tokens that are added to the output of various computations. We apply EXPRES for image classification and few-shot semantic segmentation, and show our method is capable of achieving state of the art prompt tuning on 3/3 categories of the VTAB benchmark. In addition to strong performance, we observe that our approach is an order of magnitude more prompt efficient than existing visual prompting baselines. We analytically show the computational benefits of our approach over weight space adaptation techniques like finetuning. Lastly we systematically corroborate the architectural design of our method via a series of ablation experiments.

关键词： continual low-shot meta or long-tail learning Transfer

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