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AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation

AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural R...

引用

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

作者： Jung, Hyunyoung Hui, Zhuo Luo, Lei Yang, Haitao Liu, Feng Yoo, Sungjoo Ranjan, Rakesh Demandolx, Denis Seoul Natl Univ Seoul South Korea Meta Real Labs Seoul South Korea

ISBN: (纸本)9798350301298

To apply optical flow in practice, it is often necessary to resize the input to smaller dimensions in order to reduce computational costs. However, downsizing inputs makes the estimation more challenging because objects and motion ranges become smaller. Even though recent approaches have demonstrated high-quality flow estimation, they tend to fail to accurately model small objects and precise boundaries when the input resolution is lowered, restricting their applicability to high-resolution inputs. In this paper, we introduce AnyFlow, a robust network that estimates accurate flow from images of various resolutions. By representing optical flow as a continuous coordinate-based representation, AnyFlow generates outputs at arbitrary scales from low-resolution inputs, demonstrating superior performance over prior works in capturing tiny objects with detail preservation on a wide range of scenes. We establish a new state-of-the-art performance of cross-dataset generalization on the KITTI dataset, while achieving comparable accuracy on the online benchmarks to other SOTA methods.

关键词： Low-level vision

来源：评论

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vision-Language Pseudo-Labels for Single-Positive Multi-Label Learning

Vision-Language Pseudo-Labels for Single-Positive Multi-Labe...

引用

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

作者： Xing, Xin Xiong, Zhexiao Stylianou, Abby Sastry, Srikumar Gong, Liyu Jacobs, Nathan Univ Nebraska Omaha Omaha NE 68182 USA Washington Univ St Louis St Louis MO USA St Louis Univ St Louis MO USA Oracle Inc Austin TX USA

ISBN: (纸本)9798350365474

We study a limited label problem and present a novel approach to Single-Positive Multi-label Learning. In the multi-label learning setting, a model learns to predict multiple labels or categories for a single input image. This contrasts with standard multi-class image classification, where the task is to predict a single label from many possible labels for an image. Single-Positive Multi-label Learning specifically considers learning to predict multiple labels when there is only one annotation per image in the training data. Multi-label learning is a more natural task than single-label learning because real-world data often involves instances belonging to multiple categories simultaneously;however, most computer vision datasets contain single labels due to the inherent complexity and cost of collecting multiple high-quality annotations per image. We propose a novel approach called vision-Language Pseudo-Labeling, which uses a vision-language model, CLIP, to suggest strong positive and negative pseudo-labels. The experiment performance shows the effectiveness of the proposed model. Our code and data will be made publicly available at https://***/mvrl/VLPL.

关键词： CLIP Pseudo-labeling Single-Positive Multi-label Learning

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Prompt-Guided Zero-Shot Anomaly Action recognition using Pretrained Deep Skeleton Features

Prompt-Guided Zero-Shot Anomaly Action Recognition using Pre...

引用

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

作者： Sato, Fumiaki Hachiuma, Ryo Sekii, Taiki Konica Minolta Inc Tokyo Japan

ISBN: (纸本)9798350301298

This study investigates unsupervised anomaly action recognition, which identifies video-level abnormal-human-behavior events in an unsupervised manner without abnormal samples, and simultaneously addresses three limitations in the conventional skeleton-based approaches: target domain-dependent DNN training, robustness against skeleton errors, and a lack of normal samples. We present a unified, user prompt-guided zero-shot learning framework using a target domain-independent skeleton feature extractor, which is pretrained on a large-scale action recognition dataset. Particularly, during the training phase using normal samples, the method models the distribution of skeleton features of the normal actions while freezing the weights of the DNNs and estimates the anomaly score using this distribution in the inference phase. Additionally, to increase robustness against skeleton errors, we introduce a DNN architecture inspired by a point cloud deep learning paradigm, which sparsely propagates the features between joints. Furthermore, to prevent the unobserved normal actions from being misidentified as abnormal actions, we incorporate a similarity score between the user prompt embeddings and skeleton features aligned in the common space into the anomaly score, which indirectly supplements normal actions. On two publicly available datasets, we conduct experiments to test the effectiveness of the proposed method with respect to abovementioned limitations.

关键词： Video: Action and event understanding

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Multi-modal Arousal and Valence Estimation under Noisy Conditions

Multi-modal Arousal and Valence Estimation under Noisy Condi...

引用

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

作者： Dresvyanskiy, Denis Markitantov, Maxim Yu, Jiawei Kaya, Heysem Karpov, Alexey Ulm Univ Ulm Germany ITMO Univ St Petersburg Russia Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg Russia Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands

ISBN: (纸本)9798350365474

Automatic emotion recognition has gained significant attention over the past two decades due to the central role that emotions play in human communication. While multi-modal systems demonstrate high performances on laboratory-controlled data, their validity on non-lab-controlled, namely 'in-the-wild' data, remains a challenge. This work investigates audio-visual deep learning approaches for emotion recognition in-the-wild, with a particular focus on the effectiveness of architectures based on fine-tuned Convolutional Neural Networks (CNN) and Public Dimensional Emotion Model (PDEM) for video and audio modality, respectively. We explore and compare various temporal modeling techniques (e.g., transformer architectures) and fusion strategies by leveraging the embeddings from developed multi-stage trained modality-specific Deep Neural Networks (DNN). The results are reported on the AffWild2 dataset following the Affective Behavior Analysis inthe-Wild 2024 (ABAW'24) challenge protocol. Our investigation highlights the complexities of robust multi-modal emotion recognition in an unconstrained environment, providing insights into the usage of various deep learning architectures for tackling this challenging task.

关键词： ABAW24 Deep Neural Networks Emotion recognition Multi-modal Valence-Arousal Estimation

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Weakly Supervised Video Emotion Detection and Prediction via Cross-Modal Temporal Erasing Network

Weakly Supervised Video Emotion Detection and Prediction via...

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

作者： Zhang, Zhicheng Wang, Lijuan Yang, Jufeng Nankai Univ Coll Comp Sci TMCC Tianjin Peoples R China

ISBN: (纸本)9798350301298

Automatically predicting the emotions of user-generated videos (UGVs) receives increasing interest recently. However, existing methods mainly focus on a few key visual frames, which may limit their capacity to encode the context that depicts the intended emotions. To tackle that, in this paper, we propose a cross-modal temporal erasing network that locates not only keyframes but also context and audio-related information in a weakly-supervised manner. In specific, we first leverage the intra- and inter-modal relationship among different segments to accurately select keyframes. Then, we iteratively erase keyframes to encourage the model to concentrate on the contexts that include complementary information. Extensive experiments on three challenging video emotion benchmarks demonstrate that our method performs favorably against state-of-the-art approaches. The code is released on https://***/nku-zhichengzhang/WECL.

关键词： vision applications and systems

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On the Benefits of 3D Pose and Tracking for Human Action recognition

On the Benefits of 3D Pose and Tracking for Human Action Rec...

引用

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

作者： Rajasegaran, Jathushan Pavlakos, Georgios Kanazawa, Angjoo Feichtenhofer, Christoph Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA Meta AI FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9798350301298

In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In this spirit, first we show the benefits of using 3D pose to infer actions, and study person-person interactions. Subsequently, we propose a Lagrangian Action recognition model by fusing 3D pose and contextualized appearance over tracklets. To this end, our method achieves state-of-the-art performance on the AVA v2.2 dataset on both pose only settings and on standard benchmark settings. When reasoning about the action using only pose cues, our pose model achieves +10.0 mAP gain over the corresponding state-of-the-art while our fused model has a gain of +2.8 mAP over the best state-of-the-art model. Code and results are available at: https://***/LART

关键词： Video: Action and event understanding

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

作者： Sharma, Aaryam Czarnecki, Chris Chen, Yuhao Xi, Pengcheng Xu, Linlin Wong, Alexander Univ Waterloo Vis & Image Proc Lab Waterloo ON Canada Natl Res Council Canada Ottawa ON Canada

ISBN: (纸本)9798350365474

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 estimation food nutrition volumetric

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Fresnel Microfacet BRDF: Unification of Polari-Radiometric Surface-Body Reflection

Fresnel Microfacet BRDF: Unification of Polari-Radiometric S...

引用

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

作者： Ichikawa, Tomoki Fukao, Yoshiki Nobuhara, Shohei Nishino, Ko Kyoto Univ Grad Sch Informat Kyoto Japan

ISBN: (纸本)9798350301298

computer vision applications have heavily relied on the linear combination of Lambertian diffuse and microfacet specular reflection models for representing reflected radiance, which turns out to be physically incompatible and limited in applicability. In this paper, we derive a novel analytical reflectance model, which we refer to as Fresnel Microfacet BRDF model, that is physically accurate and generalizes to various real-world surfaces. Our key idea is to model the Fresnel reflection and transmission of the surface microgeometry with a collection of oriented mirror facets, both for body and surface reflections. We carefully derive the Fresnel reflection and transmission for each microfacet as well as the light transport between them in the subsurface. This physically-grounded modeling also allows us to express the polarimetric behavior of reflected light in addition to its radiometric behavior. That is, FMBRDF unifies not only body and surface reflections but also light reflection in radiometry and polarization and represents them in a single model. Experimental results demonstrate its effectiveness in accuracy, expressive power, image-based estimation, and geometry recovery.

关键词： Physics-based vision and shape-from-X

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Domain Targeted Synthetic Plant Style Transfer using Stable Diffusion, LoRA and ControlNet

Domain Targeted Synthetic Plant Style Transfer using Stable ...

引用

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

作者： Hartley, Zane K. J. Lind, Rob J. Pound, Michael P. French, Andrew P. Univ Nottingham Wollaton Rd Nottingham NG8 1BB England Syngenta Jealotts Hill Int Res Ctr Warfield England

ISBN: (纸本)9798350365474

Synthetic images can help alleviate much of the cost in the creation of training data for plant phenotyping-focused AI development. Synthetic-to-real style transfer is of particular interest to users of artificial data because of the domain shift problem created by training neural networks on images generated in a digital environment. In this paper we present a pipeline for synthetic plant creation and image-to-image style transfer, with a particular interest in synthetic to real domain adaptation targeting specific real datasets. Utilizing new advances in generative AI, we employ a combination of Stable diffusion, Low Ranked Adapters (LoRA) and ControlNets to produce an advanced system of style transfer. We focus our work on the core task of leaf instance segmentation, exploring both synthetic to real style transfer as well as inter-species style transfer and find that our pipeline makes numerous improvements over CycleGAN for style transfer, and the images we produce are comparable to real images when used as training data.

关键词： Agriculture computer vision ControlNet Deep Learning Diffusion LoRA Plant Phenotyping

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Towards Engineered Safe AI with Modular Concept Models

Towards Engineered Safe AI with Modular Concept Models

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

作者： Heidemann, Lena Kurzidem, Iwo Monnet, Maureen Roscher, Karsten Guennemann, Stephan Fraunhofer IKS Munich Germany Tech Univ Munich Munich Germany

ISBN: (纸本)9798350365474

The inherent complexity and uncertainty of Machine Learning (ML) makes it difficult for ML-based computer vision (CV) approaches to become prevalent in safety-critical domains like autonomous driving, despite their high performance. A crucial challenge in these domains is the safety assurance of ML-based systems. To address this, recent safety standardization in the automotive domain has introduced an ML safety lifecycle following an iterative development process. While this approach facilitates safety assurance, its iterative nature requires frequent adaptation and optimization of the ML function, which might include costly retraining of the ML model and is not guaranteed to converge to a safe AI solution. In this paper, we propose a modular ML approach which allows for more efficient and targeted measures to each of the modules and process steps. Each module of the modular concept model represents one visual concept and is aggregated with the other modules' outputs into a task output. The design choices of a modular concept model can be categorized into the selection of the concept modules, the aggregation of their output and the training of the concept modules. Using the example of traffic sign classification, we present each step of the involved design choices and the corresponding targeted measures to take in an iterative development process for engineering safe AI.

关键词： computer vision Concept Models Deep Neural Networks Explainable AI Interpretable Models Machine Learning ML Safety Modular Concept Models Modular Deep Learning Safe AI Safe ML

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