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A Perspective on Deep vision Performance with Standard Image and Video Codecs

A Perspective on Deep Vision Performance with Standard Image...

引用

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

作者： Reich, Christoph Hahn, Oliver Cremers, Daniel Roth, Stefan Debnath, Biplob Tech Univ Darmstadt Darmstadt Germany Tech Univ Munich Munich Germany NEC Labs Amer Inc San Jose CA 95110 USA Hessian Ctr AI Hessian AI Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany

ISBN: (纸本)9798350365474

Resource-constrained hardware, such as edge devices or cell phones, often rely on cloud servers to provide the required computational resources for inference in deep vision models. However, transferring image and video data from an edge or mobile device to a cloud server requires coding to deal with network constraints. The use of standardized codecs, such as JPEG or H.264, is prevalent and required to ensure interoperability. This paper aims to examine the implications of employing standardized codecs within deep vision pipelines. We find that using JPEG and H.264 coding significantly deteriorates the accuracy across a broad range of vision tasks and models. For instance, strong compression rates reduce semantic segmentation accuracy by more than 80% in mIoU. In contrast to previous findings, our analysis extends beyond image and action classification to localization and dense prediction tasks, thus providing a more comprehensive perspective.

关键词： Image Classification Image Compression Object Detectio Optical Flow Estimation Semantic Segmentation Video Compression

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VLM-PL: Advanced Pseudo Labeling approach for Class Incremental Object Detection via vision-Language Model

VLM-PL: Advanced Pseudo Labeling approach for Class Incremen...

引用

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

作者： Kim, Junsu Ku, Yunhoe Kim, Jihyeon Cha, Junuk Baek, Seungryul UNIST Ulsan South Korea MODULABS Seoul South Korea

ISBN: (纸本)9798350365474

In the field of Class Incremental Object Detection (CIOD), creating models that can continuously learn like humans is a major challenge. Pseudo-labeling methods, although initially powerful, struggle with multi-scenario incremental learning due to their tendency to forget past knowledge. To overcome this, we introduce a new approach called vision-Language Model assisted Pseudo-Labeling (VLM-PL). This technique uses vision-Language Model (VLM) to verify the correctness of pseudo ground-truths (GTs) without requiring additional model training. VLM-PL starts by deriving pseudo GTs from a pre-trained detector. Then, we generate custom queries for each pseudo GT using carefully designed prompt templates that combine image and text features. This allows the VLM to classify the correctness through its responses. Furthermore, VLM-PL integrates refined pseudo and real GTs from upcoming training, effectively combining new and old knowledge. Extensive experiments conducted on the Pascal VOC and MS COCO datasets not only highlight VLM-PL's exceptional performance in multi-scenario but also illuminate its effectiveness in dual-scenario by achieving state-of-the-art results in both.

关键词： CIOD Class Incremental Object Detection Continual Learning Incremental Learning Object Detection Pseudo Labeling vision-Language Model

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Evaluating Confidence Calibration in Endoscopic Diagnosis Models

Evaluating Confidence Calibration in Endoscopic Diagnosis Mo...

引用

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

作者： Dehghani, Nikoo Thijssen, Ayla van der Zander, Quirine E. W. Schreuder, Ramon-Michel Schoon, Erik J. van der Sommen, Fons de With, Peter H. N. Eindhoven Univ Technol Eindhoven Netherlands Maastricht Univ Med Ctr Maastricht Netherlands GROW Res Inst Oncol & Reprod Maastricht Netherlands Catharina Hosp Eindhoven Netherlands Eindhoven Artificial Intelligence Syst Inst Eindhoven Netherlands

ISBN: (纸本)9798350365474

Colorectal polyps are prevalent precursors to colorectal cancer, making their accurate characterization essential for timely intervention and patient outcomes. Deep learning-based computer-aided diagnosis (CADx) systems have shown promising performance in the automated detection and categorization of colorectal polyps (CRP) using endoscopic images. However, alongside the advancement in diagnostic accuracy, the need for reliable and accurate quantification of uncertainty estimates within these systems has become increasingly important. The primary focus of this study is on refining the reliability of computer-aided diagnosis of CRPs within clinical practice. We perform an investigation of widely used model calibration techniques and how they translate into clinical applications, specifically for CRP categorization data. The experiments reveal that the Variational Inference method excels in intra-dataset calibration, but lacks efficiency and inter-dataset generalization. Laplace approximation and temperature scaling methods offer improved calibration across datasets.

关键词： Bayesian neural networks computer-aided diagnosis Confidence calibration Model reliability

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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

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

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

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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...

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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...

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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

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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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