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What does CLIP know about peeling a banana?

What does CLIP know about peeling a banana?

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Claudia Cuttano Gabriele Rosi Gabriele Trivigno Giuseppe Averta Politecnico di Torino Focoos AI

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Humans show an innate capability to identify tools to support specific actions. The association between objects parts and the actions they facilitate is usually named affordance. Being able to segment objects parts depending on the tasks they afford is crucial to enable intelligent robots to use objects of daily living. Traditional supervised learning methods for affordance segmentation require costly pixel-level annotations, while weakly supervised approaches, though less demanding, still rely on object-interaction examples and support a closed set of actions. These limitations hinder scalability, may introduce biases, and usually restrict models to a limited set of predefined actions. This paper proposes Affordance-CLIP, to overcome these limitations by leveraging the implicit affordance knowledge embedded within large pre-trained vision-Language models like CLIP. We experimentally demonstrate that CLIP, although not explicitly trained for affordances detection, retains valuable information for the task. Our AffordanceCLIP achieves competitive zero-shot performance compared to methods with specialized training, while offering several advantages: i) it works with any action prompt, not just a predefined set; ii) it requires training only a small number of additional parameters compared to existing solutions and iii) eliminates the need for direct supervision on action-object pairs, opening new perspectives for functionality-based reasoning of models.

关键词： Training computer vision Affordances Scalability conferences Computational modeling Supervised learning

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The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol

The devil is in discretization discrepancy. Robustifying Dif...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Konstanty Subbotko Wojciech Jablonski Piotr Bilinski University of Warsaw

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization error, which can severely damage the process of obtaining the final architecture. In our work, we first study the risk of discretization error and show how it affects an unregularized supernet. Then, we present that penalizing high entropy, a common technique of architecture regularization, can hinder the supernet’s performance. Therefore, to robustify the DNAS framework, we introduce a novel single-stage searching protocol, which is not reliant on decoding a continuous architecture. Our results demonstrate that this approach outperforms other DNAS methods by achieving 75.3% in the searching stage on the Cityscapes validation dataset and attains performance 1.1% higher than the optimal network of DCNAS on the non-dense search space comprising short connections. The entire training process takes only 5.5 GPU days due to the weight reuse, and yields a computationally efficient architecture. Additionally, we propose a new dataset split procedure, which substantially improves results and prevents architecture degeneration in DARTS.

关键词： Training computer vision Protocols Neural networks DNA Graphics processing units computer architecture

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Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face recognition

Identity-driven Three-Player Generative Adversarial Network ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jan Niklas Kolf Tim Rieber Jurek Elliesen Fadi Boutros Arjan Kuijper Naser Damer Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Department of Computer Science TU Darmstadt Darmstadt Germany

Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available 1 .

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ViTKD: Feature-based Knowledge Distillation for vision Transformers

ViTKD: Feature-based Knowledge Distillation for Vision Trans...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhendong Yang Zhe Li Ailing Zeng Zexian Li Chun Yuan Yu Li Tsinghua Shenzhen International Graduate School International Digital Economy Academy (IDEA) Institute of Automation Chinese Academy of Sciences Beihang University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Knowledge Distillation (KD) has been extensively studied as a means to enhance the performance of smaller models in Convolutional Neural Networks (CNNs). Recently, the vision Transformer (ViT) has demonstrated remarkable success in various computer vision tasks, leading to an increased demand for KD in ViT. However, while logit-based KD has been applied to ViT, other feature-based KD methods for CNNs cannot be directly implemented due to the significant structure gap. In this paper, we conduct an analysis of the properties of different feature layers in ViT to identify a method for feature-based ViT distillation. Our findings reveal that both shallow and deep layers in ViT are equally important for distillation and require distinct distillation strategies. Based on these guidelines, we propose our feature-based method ViTKD, which mimics the shallow layers and generates the deep layer in the teacher. ViTKD leads to consistent and significant improvements in the students. On ImageNet-1K, we achieve performance boosts of 1.64% for DeiT-Tiny, 1.40% for DeiT-Small, and 1.70% for DeiT-Base. Downstream tasks also demonstrate the superiority of ViTKD. Additionally, ViTKD and logit-based KD are complementary and can be applied together directly, further enhancing the student’s performance. Specifically, DeiT-T, S, and B achieve accuracies of 77.78%, 83.59%, and 85.41%, respectively, using this combined approach. Code is available at https://***/yzd-v/cls_KD.

关键词： Knowledge engineering computer vision conferences Computational modeling MIMICs Transformers pattern recognition

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How Much You Ate? Food Portion Estimation on Spoons

How Much You Ate? Food Portion Estimation on Spoons

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Aaryam Sharma Chris Czarnecki Yuhao Chen Pengcheng Xi Linlin Xu Alexander Wong Vision and Image Processing Lab University of Waterloo National Research Council Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil’s surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.

关键词： computer vision Image segmentation Accuracy Pipelines Estimation Image capture Cameras

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nnMobileNet: Rethinking CNN for Retinopathy Research

nnMobileNet: Rethinking CNN for Retinopathy Research

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wenhui Zhu Peijie Qiu Xiwen Chen Xin Li Natasha Lepore Oana M. Dumitrascu Yalin Wang School of Computing and Augmented Intelligence Arizona State University McKeley School of Engineering Washington University in St. Louis School of Computing Clemson University Department of Radiology Children’s Hospital Los Angeles CIBORG Lab Department of Neurology Mayo Clinic

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Over the past few decades, convolutional neural networks (CNNs) have been at the forefront of the detection and tracking of various retinal diseases (RD). Despite their success, the emergence of vision transformers (ViT) in the 2020s has shifted the trajectory of RD model development. The leading-edge performance of ViT-based models in RD can be largely credited to their scalability—their ability to improve as more parameters are added. As a result, ViT-based models tend to outshine traditional CNNs in RD applications, albeit at the cost of increased data and computational demands. ViTs also differ from CNNs in their approach to processing images, working with patches rather than local regions, which can complicate the precise localization of small, variably presented lesions in RD. In our study, we revisited and updated the architecture of a CNN model, specifically MobileNet, to enhance its utility in RD diagnostics. We found that an optimized MobileNet, through selective modifications, can surpass ViT-based models in various RD benchmarks, including diabetic retinopathy grading, detection of multiple fundus diseases, and classification of diabetic macular edema. The code is available at https://***/Retinal-Research/NN-MOBILENET

关键词： Location awareness computer vision Computational modeling Transformers Retina Trajectory pattern recognition

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Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent Units

Neuromorphic Lip-Reading with Signed Spiking Gated Recurrent...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Manon Dampfhoffer Thomas Mesquida CEA List Univ. Grenoble Alpes Grenoble France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Automatic Lip-Reading (ALR) requires the recognition of spoken words based on a visual recording of the speaker’s lips, without access to the sound. ALR with neuromorphic event-based vision sensors, instead of traditional frame-based cameras, is particularly promising for edge applications due to their high temporal resolution, low power consumption and robustness. Neuromorphic models, such as Spiking Neural Networks (SNNs), encode information using events and are naturally compatible with such data. The sparse and event-based nature of both the sensor data and SNN activations can be leveraged in an end-to-end neuromorphic hardware pipeline for low-power and low-latency edge applications. However, the accuracy of SNNs is often largely degraded compared to state-of-the-art non-spiking Artificial Neural Networks (ANNs). In this work, a new SNN model, the Signed Spiking Gated Recurrent Unit (SpikGRU2+), is proposed and used as a task head for event-based ALR. The SNN architecture is as accurate as its ANN equivalent, and outperforms the state-of-the-art on the DVS-Lip dataset. Notably, the accuracy is improved by 25% (respectively 4%) compared to the previous state-of-the-art SNN (respectively ANN). In addition, the SNN spike sparsity can be optimized to further reduce the number of operations up to 22x compared to the ANN while maintaining a high accuracy. This work opens up new perspectives for the use of SNNs for accurate and low-power end-to-end neuromorphic gesture recognition. Code is available 1 .

关键词： Visualization Accuracy Neuromorphics Gesture recognition Spiking neural networks Logic gates vision sensors

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Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNets

Conv-Adapter: Exploring Parameter Efficient Transfer Learnin...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hao Chen Ran Tao Han Zhang Yidong Wang Xiang Li Wei Ye Jindong Wang Guosheng Hu Marios Savvides Carneige Mellon University Peking University Microsoft Research Asia Oosto

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While parameter efficient tuning (PET) methods have shown great potential with transformer architecture on Natural Language Processing (NLP) tasks, their effectiveness with large-scale ConvNets is still under-studied on computer vision (CV) tasks. This paper proposes Conv-Adapter, a PET module designed for ConvNets. Conv-Adapter is light-weight, domain-transferable, and architecture-agnostic with generalized performance on different tasks. When transferring on downstream tasks, Conv- Adapter learns tasks-specific feature modulation to the intermediate representations of backbones while keeping the pre-trained parameters frozen. By introducing only a tiny amount of learnable parameters, e.g., only 3.5% full finetuning parameters of ResNet50. It can also be applied for transformer-based backbones. Conv-Adapter outperforms previous PET baseline methods and achieves comparable or surpasses the performance of full fine-tuning on 23 classification tasks of various domains. It also presents superior performance on the few-shot classification with an average margin of 3.39%. Beyond classification, Conv-Adapter can generalize to detection and segmentation tasks with more than 50% reduction of parameters but comparable performance to the traditional full fine-tuning 1

关键词： computer vision conferences Transfer learning Modulation Transformers Natural language processing Robustness

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VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

VT-Former: An Exploratory Study on Vehicle Trajectory Predic...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Armin Danesh Pazho Ghazal Alinezhad Noghre Vinit Katariya Hamed Tabkhi University of North Carolina Charlotte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims to forecast a vehicle’s future positions based on its past and current movements. VTP is a pivotal element for road safety, aiding in applications such as traffic management, accident prevention, work-zone safety, and energy optimization. While most works in this field focus on autonomous driving, with the growing number of surveillance cameras, another sub-field emerges for surveillance VTP with its own set of challenges. In this paper, we introduce VT-Former, a novel transformer-based VTP approach for highway safety and surveillance. In addition to utilizing transformers to capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module has been proposed to capture intricate social interactions among vehicles. This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP. Our investigation, conducted across three benchmark datasets from diverse surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable performance of VT-Former in predicting vehicle trajectories. This study underscores the potential of VT-Former and its architecture, opening new avenues for future research and exploration.

关键词： Road transportation computer vision Surveillance Computational modeling computer architecture Benchmark testing Transformers

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VolRAFT: Volumetric Optical Flow Network for Digital Volume Correlation of Synchrotron Radiation-based Micro-CT Images of Bone-Implant Interfaces

VolRAFT: Volumetric Optical Flow Network for Digital Volume ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Tak Ming Wong Julian Moosmann Berit Zeller-Plumhoff Institute of Metallic Biomaterials Helmholtz-Zentrum Hereon Germany Institute of Materials Physics Helmholtz-Zentrum Hereon Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In materials science research, digital volume correlation (DVC) analysis is commonly used to track deformations and strains to elucidate morphology-function relationships. Optical flow-based DVC is particularly popular because of its robustness to estimate the correlation as a dense deformation vector. Recently, computer vision researchers showed that network-based optical flow approaches can outperform classical iterative optical flow approaches. In this paper, we propose a supervised machine learning approach for digital volume correlation, VolRAFT, that estimates the 3D displacement vector between the reference volume and the deformed volume. The proposed approach extends the state-of-the-art network-based optical flow method, RAFT, from 2D images to 3D volumes such that it predicts the volumetric displacement vector from the input volume pairs. Experiments show that the proposed network performs well in estimating different displacement fields when compared to cutting-edge iterative DVC methods for bone-implant materials based on high resolution synchrotron-radiation micro-computed tomography imaging data.

关键词： Training computer vision Materials science and technology Correlation Three-dimensional displays Deformation Tomography

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