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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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Toward Accurate Post-Training Quantization for Image Super Resolution

Toward Accurate Post-Training Quantization for Image Super R...

引用

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

作者： Tu, Zhijun Hu, Jie Chen, Hanting Wang, Yunhe Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Model quantization is a crucial step for deploying super resolution (SR) networks on mobile devices. However, existing works focus on quantization-aware training, which requires complete dataset and expensive computational overhead. In this paper, we study post-training quantization (PTQ) for image super resolution using only a few unlabeled calibration images. As the SR model aims to maintain the texture and color information of input images, the distribution of activations are long-tailed, asymmetric and highly dynamic compared with classification models. To this end, we introduce the density-based dual clipping to cut off the outliers based on analyzing the asymmetric bounds of activations. Moreover, we present a novel pixel aware calibration method with the supervision of the full-precision model to accommodate the highly dynamic range of different samples. Extensive experiments demonstrate that the proposed method significantly outperforms existing PTQ algorithms on various models and datasets. For instance, we get a 2.091 dB increase on Urban100 benchmark when quantizing EDSRx4 to 4-bit with 100 unlabeled images. Our code is available at both PyTorch and MindSpore.

关键词： Low-level vision

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Multimodality Helps Unimodality: Cross-Modal Few-Shot Learning with Multimodal Models

Multimodality Helps Unimodality: Cross-Modal Few-Shot Learni...

引用

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

作者： Lin, Zhiqiu Yu, Samuel Kuang, Zhiyi Pathak, Deepak Ramanan, Deva Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798350301298

The ability to quickly learn a new task with minimal instruction - known as few-shot learning - is a central aspect of intelligent agents. Classical few-shot benchmarks make use of few-shot samples from a single modality, but such samples may not be sufficient to characterize an entire concept class. In contrast, humans use cross-modal information to learn new concepts efficiently. In this work, we demonstrate that one can indeed build a better visual dog classifier by reading about dogs and listening to them bark. To do so, we exploit the fact that recent multimodal foundation models such as CLIP are inherently cross-modal, mapping different modalities to the same representation space. Specifically, we propose a simple cross-modal adaptation approach that learns from few-shot examples spanning different modalities. By repurposing class names as additional one-shot training samples, we achieve SOTA results with an embarrassingly simple linear classifier for vision-language adaptation. Furthermore, we show that our approach can benefit existing methods such as prefix tuning, adapters, and classifier ensembling. Finally, to explore other modalities beyond vision and language, we construct the first (to our knowledge) audiovisual few-shot benchmark and use cross-modal training to improve the performance of both image and audio classification. Project site at link.

关键词： detection recognition: Categorization retrieval

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LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression

LVQAC: Lattice Vector Quantization Coupled with Spatially Ad...

引用

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

作者： Zhang, Xi Wu, Xiaolin Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Hamilton ON Canada

ISBN: (纸本)9798350301298

Recently, numerous end-to-end optimized image compression neural networks have been developed and proved themselves as leaders in rate-distortion performance. The main strength of these learnt compression methods is in powerful nonlinear analysis and synthesis transforms that can be facilitated by deep neural networks. However, out of operational expediency, most of these end-to-end methods adopt uniform scalar quantizers rather than vector quantizers, which are information-theoretically optimal. In this paper, we present a novel Lattice Vector Quantization scheme coupled with a spatially Adaptive Companding (LVQAC) mapping. LVQ can better exploit the inter-feature dependencies than scalar uniform quantization while being computationally almost as simple as the latter. Moreover, to improve the adaptability of LVQ to source statistics, we couple a spatially adaptive companding (AC) mapping with LVQ. The resulting LVQAC design can be easily embedded into any end-to-end optimized image compression system. Extensive experiments demonstrate that for any end-to-end CNN image compression models, replacing uniform quantizer by LVQAC achieves better rate-distortion performance without significantly increasing the model complexity.

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...

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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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A Simple Framework for Text-Supervised Semantic Segmentation

A Simple Framework for Text-Supervised Semantic Segmentation

引用

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

作者： Yi, Muyang Cui, Quan Wu, Hao Yang, Cheng Yoshie, Osamu Lu, Hongtao Shanghai Jiao Tong Univ AI Inst Dept Comp Sci & Engn MoE Key Lab Artificial Intelligence Shanghai Peoples R China Waseda Univ Tokyo Japan ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798350301298

Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an effective text-supervised semantic segmentor by itself. First, we reveal that a vanilla CLIP is inferior to localization and segmentation due to its optimization being driven by densely aligning visual and language representations. Second, we propose the locality-driven alignment (LoDA) to address the problem, where CLIP optimization is driven by sparsely aligning local representations. Third, we propose a simple segmentation (SimSeg) framework. LoDA and SimSeg jointly ameliorate a vanilla CLIP to produce impressive semantic segmentation results. Our method outperforms previous state-of-the-art methods on PASCAL VOC 2012, PASCAL Context and COCO datasets by large margins. Code and models are available at ***/muyangyi/SimSeg.

关键词： and reasoning language vision

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NTIRE 2024 Challenge on Light Field Image Super-Resolution: Methods and Results

NTIRE 2024 Challenge on Light Field Image Super-Resolution: ...

引用

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

作者： Wang, Yingqian Liang, Zhengyu Chen, Qianyu Wang, Longguang Yang, Jungang Timofte, Radu Guo, Yulan Chao, Wentao Kan, Yiming Wang, Xuechun Duan, Fuqing Wang, Guanghui Xia, Wang Wang, Ziqi Yan, Yue Xia, Peiqi Wang, Shunzhou Lu, Yao Yang, Angulia Jin, Kai Wei, Zeqiang Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Yu, Zhongxin Luo, Shaofei Zhong, Cheng Chen, Shaorui Peng, Long He, Yuhong Liu, Gaosheng Yue, Huanjing Yang, Jingyu Yao, Zhengjian Hu, Jiakui Jin, Lujia Liu, Zhi-Song He, Chenhang Xiao, Jun Wang, Xiuyuan Tian, Zonglin Mao, Yifan Liu, Deyang Li, Shizheng An, Ping Natl Univ Def Technol Changsha Peoples R China Aviat Univ Air Force Changchun Peoples R China Univ Wurzburg Comp Vis Lab Wurzburg Germany Beijing Normal Univ Beijing Peoples R China Toronto Metropolitan Univ Toronto ON Canada Beijing Inst Technol Beijing Peoples R China Shenzhen MSU BIT Univ Shenzhen Peoples R China Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Big Technol Pte Ltd Shanghai Peoples R China Peking Univ Inst Digital Media Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Fujian Normal Univ Fuzhou Peoples R China Univ Sci & Technol China Hefei Peoples R China Northeastern Univ Boston MA 02115 USA Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Peking Univ Beijing Peoples R China Lappeenranta Lahti Univ Technol LUT Lappeenranta Finland Hong Kong Polytech Univ Hong Kong Peoples R China Anqing Normal Univ Anqing Peoples R China Shanghai Univ Shanghai Peoples R China

ISBN: (纸本)9798350365474

In this report, we summarize the 2nd NTIRE challenge on light field (LF) image super-resolution (SR) with a focus on new methods and results. This challenge aims at super-resolving LF images under the standard bicubic downsampling degradation with a magnification factor of x4. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. This year of challenge has two tracks, including one track on fidelity (i.e., restoration accuracy in terms of PSNR) only, and the other track on fidelity with an extra constraint on model size and computational cost. In total, 125 participants were successfully registered for this challenge, and 9 teams have successfully submitted results with PSNR scores higher than the baseline methods. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR.

关键词： challenge light field super-resolution

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Data-driven Feature Tracking for Event Cameras

Data-driven Feature Tracking for Event Cameras

引用

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

作者： Messikommer, Nico Fang, Carter Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.

关键词： Low-level vision

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Ambiguous Medical Image Segmentation using Diffusion Models

Ambiguous Medical Image Segmentation using Diffusion Models

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

作者： Rahman, Aimon Valanarasu, Jeya Maria Jose Hacihaliloglu, Ilker Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Univ British Columbia Vancouver BC Canada

ISBN: (纸本)9798350301298

Collective insights from a group of experts have always proven to outperform an individual's best diagnostic for clinical tasks. For the task of medical image segmentation, existing research on AI-based alternatives focuses more on developing models that can imitate the best individual rather than harnessing the power of expert groups. In this paper, we introduce a single diffusion model-based approach that produces multiple plausible outputs by learning a distribution over group insights. Our proposed model generates a distribution of segmentation masks by leveraging the inherent stochastic sampling process of diffusion using only minimal additional learning. We demonstrate on three different medical image modalities- CT, ultrasound, and MRI that our model is capable of producing several possible variants while capturing the frequencies of their occurrences. Comprehensive results show that our proposed approach outperforms existing state-of-the-art ambiguous segmentation networks in terms of accuracy while preserving naturally occurring variation. We also propose a new metric to evaluate the diversity as well as the accuracy of segmentation predictions that aligns with the interest of clinical practice of collective insights. Implementation code: https://***/aimansnigdha/Ambiguous-Medical-Image-Segmentation-using-Diffusion-Models.

关键词： cell microscopy Medical and biological vision

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Affordance Grounding from Demonstration Video to Target Image

Affordance Grounding from Demonstration Video to Target Imag...

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

作者： Chen, Joya Gao, Difei Lin, Kevin Qinghong Shou, Mike Zheng Natl Univ Singapore Show Lab Singapore Singapore

ISBN: (纸本)9798350301298

Humans excel at learning from expert demonstrations and solving their own problems. To equip intelligent robots and assistants, such as AR glasses, with this ability, it is essential to ground human hand interactions (i.e., affordances) from demonstration videos and apply them to a target image like a user's AR glass view. This video-to-image affordance grounding task is challenging due to (1) the need to predict fine-grained affordances, and (2) the limited training data, which inadequately covers video-image discrepancies and negatively impacts grounding. To tackle them, we propose Affordance Transformer (Afformer), which has a fine-grained transformer-based decoder that gradually refines affordance grounding. Moreover, we introduce Mask Affordance Hand (MaskAHand), a self-supervised pre-training technique for synthesizing video-image data and simulating context changes, enhancing affordance grounding across video-image discrepancies. Afformer with MaskAHand pre-training achieves state-of-the-art performance on multiple benchmarks, including a substantial 37% improvement on the OPRA dataset. Code is made available at https://***/showlab/afformer.

关键词： vision applications and systems

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