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There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge

There is More than Meets the Eye: Self-Supervised Multi-Obje...

引用

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

作者： Valverde, Francisco Rivera Hurtado, Juana Valeria Valada, Abhinav Univ Freiburg Freiburg Germany

ISBN: (纸本)9781665445092

Attributes of sound inherent to objects can provide valuable cues to learn rich representations for object detection and tracking. Furthermore, the co-occurrence of audiovisual events in videos can be exploited to localize objects over the image field by solely monitoring the sound in the environment. Thus far, this has only been feasible in scenarios where the camera is static and for single object detection. Moreover, the robustness of these methods has been limited as they primarily rely on RGB images which are highly susceptible to illumination and weather changes. In this work, we present the novel self-supervised MM-DistillNet framework consisting of multiple teachers that leverage diverse modalities including RGB, depth and thermal images, to simultaneously exploit complementary cues and distill knowledge into a single audio student network. We propose the new MTA loss function that facilitates the distillation of information from multimodal teachers in a self-supervised manner. Additionally, we propose a novel self-supervised pretext task for the audio student that enables us to not rely on labor-intensive manual annotations. We introduce a large-scale multimodal dataset with over 113,000 time-synchronized frames of RGB, depth, thermal, and audio modalities. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods while being able to detect multiple objects using only sound during inference and even while moving.

关键词： Training Visualization Object detection Robustness pattern recognition Task analysis Spectrogram

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Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging

Physics-based Iterative Projection Complex Neural Network fo...

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

作者： Zhang, Feilong Liu, Xianming Guo, Cheng Lin, Shiyi Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9781665445092

Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. However, their interpretability and generalization still remain a major challenge. In this paper, we propose to combine the advantages of both model-based alternative projection method and deep neural network for phase retrieval, so as to achieve network interpretability and inference effectiveness simultaneously. Specifically, we unfold the iterative process of the alternative projection phase retrieval into a feed-forward neural network, whose layers mimic the processing flow. The physical model of the imaging process is then naturally embedded into the neural network structure. Moreover, a complex-valued U-Net is proposed for defining image priori for forward and backward projection in dual planes. Finally, we designate physics-based formulation as an untrained deep neural network, whose weights are enforced to fit to the given intensity measurements. In summary, our scheme for phase retrieval is effective, interpretable, physics-based and unsupervised. Experimental results demonstrate that our method achieves superior performance compared with the state-of-the-arts in a practical phase retrieval application-lensless microscopy imaging.

关键词： Deep learning Weight measurement Phase measurement Microscopy Neural networks Loss measurement pattern recognition

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DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

DOTS: Decoupling Operation and Topology in Differentiable Ar...

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

作者： Gu, Yu-Chao Wang, Li-Juan Liu, Yun Yang, Yi Wu, Yu-Huan Lu, Shao-Ping Cheng, Ming-Ming Nankai Univ CS TKLNDST Tianjin Peoples R China Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9781665445092

Differentiable Architecture Search (DARTS) has attracted extensive attention due to its efficiency in searching for cell structures. DARTS mainly focuses on the operation search and derives the cell topology from the operation weights. However, the operation weights can not indicate the importance of cell topology and result in poor topology rating correctness. To tackle this, we propose to Decouple the Operation and Topology Search (DOTS), which decouples the topology representation from operation weights and makes an explicit topology search. DOTS is achieved by introducing a topology search space that contains combinations of candidate edges. The proposed search space directly reflects the search objective and can be easily extended to support a flexible number of edges in the searched cell. Existing gradient-based NAS methods can be incorporated into DOTS for further improvement by the topology search. Considering that some operations (e.g., Skip-Connection) can affect the topology, we propose a group operation search scheme to preserve topology-related operations for a better topology search. The experiments on CIFAR10/100 and ImageNet demonstrate that DOTS is an effective solution for differentiable NAS.

关键词： computer vision Codes Microprocessors Image edge detection computer architecture US Department of Transportation Search problems

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Where are my clothes? A multi-level approach for evaluating deep instance segmentation architectures on fashion images

Where are my clothes? A multi-level approach for evaluating ...

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

作者： Jouanneau, Warren Bugeau, Aurelie Palyart, Marc Papadakis, Nicolas Vezard, Laurent Univ Bordeaux LaBRI CNRS Bordeaux INP F-33400 Talence France Lectra F-33610 Cestas France Univ Bordeaux IMB CNRS Bordeaux INPUMR 5251 F-33400 Talence France

ISBN: (纸本)9781665448994

In this paper we present an extensive evaluation of instance segmentation in the context of images containing clothes. We propose a multi level evaluation that completes the classical overlapping criteria given by IoU. In particular, we quantify both the contour and color content accuracy of the the predicted segmentation masks. We demonstrate that the proposed evaluation framework is relevant to obtain meaningful insights on models performance through experiments conducted on five state of the art instance segmentation methods.

关键词： Image segmentation computer vision Image color analysis conferences Computational modeling computer architecture pattern recognition

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Trade-off between Robustness and Accuracy of vision Transformers

Trade-off between Robustness and Accuracy of Vision Transfor...

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

作者： Li, Yanxi Xu, Chang Univ Sydney Fac Engn Sch Comp Sci Sydney NSW Australia

ISBN: (纸本)9798350301298

Although deep neural networks (DNNs) have shown great successes in computer vision tasks, they are vulnerable to perturbations on inputs, and there exists a trade-off between the natural accuracy and robustness to such perturbations, which is mainly caused by the existence of robust non-predictive features and non-robust predictive features. Recent empirical analyses find vision Transformers (ViTs) are inherently robust to various kinds of perturbations, but the aforementioned trade-off still exists for them. In this work, we propose Trade-off between Robustness and Accuracy of vision Transformers (TORA-ViTs), which aims to efficiently transfer ViT models pretrained on natural tasks for both accuracy and robustness. TORA-ViTs consist of two major components, including a pair of accuracy and robustness adapters to extract predictive and robust features, respectively, and a gated fusion module to adjust the trade-off. The gated fusion module takes outputs of a pretrained ViT block as queries and outputs of our adapters as keys and values, and tokens from different adapters at different spatial locations are compared with each other to generate attention scores for a balanced mixing of predictive and robust features. Experiments on ImageNet with various robust benchmarks show that our TORA-ViTs can efficiently improve the robustness of naturally pretrained ViTs while maintaining competitive natural accuracy. Our most balanced setting (TORA-ViTs with lambda = 0.5) can maintain 83.7% accuracy on clean ImageNet and reach 54.7% and 38.0% accuracy under FGSM and PGD white-box attacks, respectively. In terms of various ImageNet variants, it can reach 39.2% and 56.3% accuracy on ImageNet-A and ImageNet-R and reach 34.4% mCE on ImageNet-C.

关键词： Adversarial attack and defense

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MAPLM: A Real-World Large-Scale vision-Language Benchmark for Map and Traffic Scene Understanding

MAPLM: A Real-World Large-Scale Vision-Language Benchmark fo...

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

作者： Cao, Xu Zhou, Tong Ma, Yunsheng Ye, Wenqian Cui, Can Tang, Kun Cao, Zhipeng Liang, Kaizhao Wang, Ziran Rehg, James M. Zheng, Chao Tencent T Lab Palo Alto CA 94306 USA Univ Illinois Champaign IL USA Purdue Univ W Lafayette IN USA Univ Virginia Charlottesville VA USA SambaNova Syst Inc Palo Alto CA USA

ISBN: (纸本)9798350353006

vision-language generative AI has demonstrated remarkable promise for empowering cross-modal scene understanding of autonomous driving and high-definition (HD) map systems. However, current benchmark datasets lack multi-modal point cloud, image, and language data pairs. Recent approaches utilize visual instruction learning and cross-modal prompt engineering to expand vision-language models into this domain. In this paper, we propose a new vision-language benchmark that can be used to finetune traffic and HD map domain-specific foundation models. Specifically, we annotate and leverage large-scale, broad-coverage traffic and map data extracted from huge HD map annotations, and use CLIP and LLaMA-2 / Vicuna to finetune a baseline model with instruction-following data. Our experimental results across various algorithms reveal that while visual instruction-tuning large language models (LLMs) can effectively learn meaningful representations from MAPLM-QA, there remains significant room for further advancements. To facilitate applying LLMs and multi-modal data into self-driving research, we will release our visual-language QA data, and the baseline models at ***/LLVM-AD/MAPLM.

关键词： High-definition (HD) Map Large Language Model Multimodal Learning vision-Language Model Visual Question Answering

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StableVITON: Learning Semantic Correspondence with Latent Diffusion Model for Virtual Try-On

StableVITON: Learning Semantic Correspondence with Latent Di...

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

作者： Kim, Jeongho Gu, Gyojung Park, Minho Park, Sunghyun Choo, Jaegul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798350353013;9798350353006

Given a clothing image and a person image, an image-based virtual try-on aims to generate a customized image that appears natural and accurately reflects the characteristics of the clothing image. In this work, we aim to expand the applicability of the pre-trained diffusion model so that it can be utilized independently for the virtual try-on task. The main challenge is to preserve the clothing details while effectively utilizing the robust generative capability of the pre-trained model. In order to tackle these issues, we propose StableVITON, learning the semantic correspondence between the clothing and the human body within the latent space of the pre-trained diffusion model in an end-to-end manner. Our proposed zero cross-attention blocks not only preserve the clothing details by learning the semantic correspondence but also generate high-fidelity images by utilizing the inherent knowledge of the pre-trained model in the warping process. Through our proposed novel attention total variation loss and applying augmentation, we achieve the sharp attention map, resulting in a more precise representation of clothing details. StableVITON out-performs the baselines in qualitative and quantitative eval-uation, showing promising quality in arbitrary person images. Our code is available at https://***/ rlawjdghek/StableVITON.

关键词： computer vision Diffusion Model Virtual Try-On

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Hierarchical Video Prediction using Relational Layouts for Human-Object Interactions

Hierarchical Video Prediction using Relational Layouts for H...

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

作者： Bodla, Navaneeth Shrivastava, Gaurav Chellappa, Rama Shrivastava, Abhinav Univ Maryland College Pk MD 20742 USA Johns Hopkins Univ Baltimore MD USA

ISBN: (纸本)9781665445092

Learning to model and predict how humans interact with objects while performing an action is challenging, and most of the existing video prediction models are ineffective in modeling complicated human-object interactions. Our work builds on hierarchical video prediction models, which disentangle the video generation process into two stages: predicting a high-level representation, such as pose sequence, and then learning a pose-to-pixels translation model for pixel generation. An action sequence for a human-object interaction task is typically very complicated, involving the evolution of pose, person's appearance, object locations, and object appearances over time. To this end, we propose a Hierarchical Video Prediction model using Relational Layouts. In the first stage, we learn to predict a sequence of layouts. A layout is a high-level representation of the video containing both pose and objects' information for every frame. The layout sequence is learned by modeling the relationships between the pose and objects using relational reasoning and recurrent neural networks. The layout sequence acts as a strong structure prior to the second stage that learns to map the layouts into pixel space. Experimental evaluation of our method on two datasets, UMD-HOI and Bimanual, shows significant improvements in standard video evaluation metrics such as LPIPS, PSNR, and SSIM. We also perform a detailed qualitative analysis of our model to demonstrate various generalizations.

关键词： Measurement computer vision Recurrent neural networks Computational modeling Layout Predictive models Cognition

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DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation

DAFormer: Improving Network Architectures and Training Strat...

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

作者： Hoyer, Lukas Dai, Dengxin Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland MPI Informat Saarbrucken Germany Katholieke Univ Leuven Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

As acquiring pixel-wise annotations of real-world images for semantic segmentation is a costly process, a model can instead be trained with more accessible synthetic data and adapted to real images without requiring their annotations. This process is studied in unsupervised domain adaptation (UDA). Even though a large number of methods propose new adaptation strategies, they are mostly based on outdated network architectures. As the influence of recent network architectures has not been systematically studied, we first benchmark different network architectures for UDA and newly reveal the potential of Transformers for UDA semantic segmentation. Based on the findings, we propose a novel UDA method, DAFormer. The network architecture of DAFormer consists of a Transformer encoder and a multilevel context-aware feature fusion decoder. It is enabled by three simple but crucial training strategies to stabilize the training and to avoid overfitting to the source domain: While (1) Rare Class Sampling on the source domain improves the quality of the pseudo-labels by mitigating the confirmation bias of self-training toward common classes, (2) a Thing-Class ImageNet Feature Distance and (3) a learning rate warmup promote feature transfer from ImageNet pretraining. DAFormer represents a major advance in UDA. It improves the state of the art by 10.8 mIoU for GTA -> Cityscapes and 5.4 mIoU for Synthia -> Cityscapes and enables learning even difficult classes such as train, bus, and truck well. The implementation is available at https://***/lhoyer/DAFormer.

关键词： Training Image segmentation computer vision Annotations Semantics Network architecture Transformers

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Exposure Normalization and Compensation for Multiple-Exposure Correction

Exposure Normalization and Compensation for Multiple-Exposur...

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

作者： Huang, Jie Liu, Yajing Fu, Xueyang Zhou, Man Wang, Yang Zhao, Feng Xiong, Zhiwei Univ Sci & Technol China Hefei Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Images captured with improper exposures usually bring unsatisfactory visual effects. Previous works mainly focus on either underexposure or overexposure correction, resulting in poor generalization to various exposures. An alternative solution is to mix the multiple exposure data for training a single network. However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple-exposures, thus resulting in poor performance. The key point to address this issue lies in bridging different exposure representations. To achieve this goal, we design a multiple exposure correction framework based on an Exposure Normalization and Compensation (ENC) module. Specifically, the ENC module consists of an exposure normalization part for mapping different exposure features to the exposure-invariant feature space, and a compensation part for integrating the initial features unprocessed by the exposure normalization part to ensure the completeness of information. Besides, to further alleviate the imbalanced performance caused by variations in the optimization process, we introduce a parameter regularization fine-tuning strategy to improve the performance of the worst-performed exposure without degrading other exposures. Our model empowered by ENC outperforms the existing methods by more than 2dB and is robust to multiple image enhancement tasks, demonstrating its effectiveness and generalization capability for real-world applications. Code: https://***/KevinJ-Huang/ExposureNorm-Compensation.

关键词： Training computer vision Codes Computational modeling Visual effects pattern recognition Task analysis

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