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Evaluating the Immediate Applicability of Pose Estimation for Sign Language recognition

Evaluating the Immediate Applicability of Pose Estimation fo...

引用

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

作者： Moryossef, Amit Tsochantaridis, Ioannis Dinn, Joe Camgoez, Necati Cihan Bowden, Richard Jiang, Tao Rios, Annette Muller, Mathias Ebling, Sarah Bar Ilan Univ Ramat Gan Israel Google Mountain View CA 94043 USA Univ Surrey Guildford Surrey England Univ Zurich Zurich Switzerland

ISBN: (纸本)9781665448994

Sign languages are visual languages produced by the movement of the hands, face, and body. In this paper, we evaluate representations based on skeleton poses, as these are explainable, person-independent, privacy-preserving, low-dimensional representations. Basically, skeletal representations generalize over an individual's appearance and background, allowing us to focus on the recognition of motion. But how much information is lost by the skeletal representation? We perform two independent studies using two state-of-the-art pose estimation systems. We analyze the applicability of the pose estimation systems to sign language recognition by evaluating the failure cases of the recognition models. Importantly, this allows us to characterize the current limitations of skeletal pose estimation approaches in sign language recognition.

关键词： Analytical models Visualization computer vision Assistive technology Face recognition conferences Pose estimation

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Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement

Masked Spatial Propagation Network for Sparsity-Adaptive Dep...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Jinyoung Jun Jae-Han Lee Chang-Su Kim Korea University Gauss Labs Inc

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However, existing research on depth completion assumes that the sparsity - the number of points or LiDAR lines - is fixed for training and testing. Hence, the completion performance drops severely when the number of sparse depths changes significantly. To address this issue, we propose the sparsity-adaptive depth refinement (SDR) framework, which refines monocular depth estimates using sparse depth points. For SDR, we propose the masked spatial propagation network (MSPN) to perform SDR with a varying number of sparse depths effectively by gradually propagating sparse depth information throughout the entire depth map. Experimental results demonstrate that MPSN achieves state-of-the-art performance on both SDR and conventional depth completion scenarios. Codes are available at htt ps: / / ***/jyjunmcl/MSPN_SDR

关键词： Training computer vision Laser radar Codes Hardware Sensors pattern recognition

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Training Generative Adversarial Networks in One Stage

Training Generative Adversarial Networks in One Stage

引用

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

作者： Shen, Chengchao Yin, Youtan Wang, Xinchao Li, Xubin Song, Jie Song, Mingli Zhejiang Univ Hangzhou Peoples R China Natl Univ Singapore Singapore Singapore Alibaba Grp Hangzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (纸本)9781665445092

Generative Adversarial Networks (GANs) have demonstrated unprecedented success in various image generation tasks. The encouraging results, however, come at the price of a cumbersome training process, during which the generator and discriminator are alternately updated in two stages. In this paper, we investigate a general training scheme that enables training GANs efficiently in only one stage. Based on the adversarial losses of the generator and discriminator, we categorize GANs into two classes, Symmetric GANs and Asymmetric GANs, and introduce a novel gradient decomposition method to unify the two, allowing us to train both classes in one stage and hence alleviate the training effort. We also computationally analyze the efficiency of the proposed method, and empirically demonstrate that, the proposed method yields a solid 1.5x acceleration across various datasets and network architectures. Furthermore, we show that the proposed method is readily applicable to other adversarial-training scenarios, such as data-free knowledge distillation. The code is available at https://***/zju-vipa/OSGAN.

关键词： Training computer vision Image synthesis Network architecture Generative adversarial networks Solids Generators

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Learning Compositional Representation for 4D Captures with Neural ODE

Learning Compositional Representation for 4D Captures with N...

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

作者： Jiang, Boyan Zhang, Yinda Wei, Xingkui Xue, Xiangyang Fu, Yanwei Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Google Mountain View CA 94043 USA Fudan Univ Sch Data Sci MOE Frontiers Ctr Brain Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

ISBN: (纸本)9781665445092

Learning based representation has become the key to the success of many computer vision systems. While many 3D representations have been proposed, it is still an unaddressed problem how to represent a dynamically changing 3D object. In this paper, we introduce a compositional representation for 4D captures, i.e. a deforming 3D object over a temporal span, that disentangles shape, initial state, and motion respectively. Each component is represented by a latent code via a trained encoder. To model the motion, a neural Ordinary Differential Equation (ODE) is trained to update the initial state conditioned on the learned motion code, and a decoder takes the shape code and the updated state code to reconstruct the 3D model at each time stamp. lb this end, we propose an Identity Exchange Training (IET) strategy to encourage the network to learn effectively decoupling each component. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art deep learning based methods on 4D reconstruction, and significantly improves on various tasks, including motion transfer and completion.

关键词： Training Geometry Solid modeling computer vision Three-dimensional displays Codes Shape

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Retrieval-Augmented Embodied Agents

Retrieval-Augmented Embodied Agents

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conference on computer vision and pattern recognition (cvpr)

作者： Yichen Zhu Zhicai Ou Xiaofeng Mou Jian Tang AI Lab Midea Group

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Embodied agents operating in complex and uncertain environments face considerable challenges. While some advanced agents handle complex manipulation tasks with proficiency, their success often hinges on extensive training data to develop their capabilities. In contrast, humans typically rely on recalling past experiences and analogous situations to solve new problems. Aiming to emulate this human approach in robotics, we introduce the Retrieval-Augmented Embodied Agent (RAEA). This innovative system equips robots with a form of shared memory, significantly enhancing their performance. Our approach integrates a policy retriever, allowing robots to access relevant strategies from an external policy memory bank based on multi-modal inputs. Additionally, a policy generator is employed to assimilate these strategies into the learning process, enabling robots to formulate effective responses to tasks. Extensive testing of RAEA in both simulated and real-world scenarios demonstrates its superior performance over traditional methods, representing a major leap forward in robotic technology.

关键词： Training computer vision Knowledge acquisition Training data Fasteners Generators pattern recognition

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M3DSSD: Monocular 3D Single Stage Object Detector

M3DSSD: Monocular 3D Single Stage Object Detector

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

作者： Luo, Shujie Dai, Hang Shao, Ling Ding, Yong Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Peoples R China Zhejiang Univ Sch Micronano Elect Hangzhou Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

In this paper, we propose a Monocular 3D Single Stage object Detector (M3DSSD) with feature alignment and asymmetric non-local attention. Current anchor-based monocular 3D object detection methods suffer from feature mismatching. To overcome this, we propose a two-step feature alignment approach. In the first step, the shape alignment is performed to enable the receptive field of the feature map to focus on the pre-defined anchors with high confidence scores. In the second step, the center alignment is used to align the features at 2D/3D centers. Further, it is often difficult to learn global information and capture long-range relationships, which are important for the depth prediction of objects. Therefore, we propose a novel asymmetric non-local attention block with multi-scale sampling to extract depth-wise features. The proposed M3DSSD achieves significantly better performance than the monocular 3D object detection methods on the KITH dataset, in both 3D object detection and bird's eye view tasks.

关键词： Solid modeling computer vision Three-dimensional displays Shape Computational modeling Object detection Detectors

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vision Transformers are Parameter-Efficient Audio-Visual Learners

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

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conference on computer vision and pattern recognition (cvpr)

作者： Yan-Bo Lin Yi-Lin Sung Jie Lei Mohit Bansal Gedas Bertasius Department of Computer Science UNC Chapel Hill

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/

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Improving Image recognition by Retrieving from Web-Scale Image-Text Data

Improving Image Recognition by Retrieving from Web-Scale Ima...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Ahmet Iscen Alireza Fathi Cordelia Schmid Google Research

Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-based memory module, which learns the importance of each retrieved example from the memory. Compared to existing approaches, our method removes the influence of the irrelevant retrieved examples, and retains those that are beneficial to the input query. We also thoroughly study various ways of constructing the memory dataset. Our experiments show the benefit of using a massive-scale memory dataset of 1B image-text pairs, and demonstrate the performance of different memory representations. We evaluate our method in three different classification tasks, namely long-tailed recognition, learning with noisy labels, and fine-grained classification, and show that it achieves state-of-the-art accuracies in ImageNet-LT, Places-LT and Webvision datasets.

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What Sketch Explainability Really Means for Downstream Tasks?

What Sketch Explainability Really Means for Downstream Tasks...

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conference on computer vision and pattern recognition (cvpr)

作者： Hmrishav Bandyopadhyay Pinaki Nath Chowdhury Ayan Kumar Bhunia Aneeshan Sain Tao Xiang Yi-Zhe Song SketchX CVSSP University of Surrey United Kingdom iFlyTek-Surrey Joint Research Centre on Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we explore the unique modality of sketch for explainability, emphasising the profound impact of human strokes compared to conventional pixel-oriented studies. Beyond explanations of network behavior, we discern the genuine implications of explainability across diverse downstream sketch-related tasks. We propose a lightweight and portable explainability solution - a seamless plugin that integrates effortlessly with any pre-trained model, elim-inating the need for re-training. Demonstrating its adaptability, we present four applications: highly studied re-trieval and generation, and completely novel assisted drawing and sketch adversarial attacks. The centrepiece to our solution is a stroke-level attribution map that takes different forms when linked with downstream tasks. By addressing the inherent non-differentiability of rasterisation, we enable explanations at both coarse stroke level (SLA) and partial stroke level (P-SLA), each with its advantages for specific downstream tasks.

关键词： Adaptation models computer vision Computational modeling pattern recognition

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Specularity Factorization for Low-Light Enhancement

Specularity Factorization for Low-Light Enhancement

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conference on computer vision and pattern recognition (cvpr)

作者： Saurabh Saini P J Narayanan CVIT KCIS IIIT-Hyderabad India

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition. Our model-driven RSFNet estimates these factors by unrolling the optimization into network layers requiring only a few scalars to be learned. The resultant factors are interpretable by design and can be fused for different image enhancement tasks via a network or combined directly by the user in a controllable fashion. Based on RSFNet, we detail a zero-reference Low Light Enhancement (LLE) application trained without paired or unpaired supervision. Our system improves the state-of-the-art performance on standard benchmarks and achieves better generalization on multiple other datasets. We also integrate our factors with other task specific fusion networks for applications like deraining, deblurring and dehazlng with negligible overhead thereby highlighting the multi-domain and multi-task generalizability of our proposed RSFNet. The code and data is released for reproducibility on the project homepage 1 1 https://***/data/projects/RSFNet/.

关键词： Ethics computer vision Neural networks Estimation Multitasking Reproducibility of results pattern recognition

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