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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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ALINA: Advanced Line Identification and Notation Algorithm

ALINA: Advanced Line Identification and Notation Algorithm

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Khan, Mohammed Abdul Hafeez Ganeriwala, Parth Bhattacharyya, Siddhartha Neogi, Natasha Muthalagu, Raja Florida Inst Technol Melbourne FL 32901 USA NASA Langley Res Ctr Hampton VA 23665 USA BITS Pilani Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9798350365474

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

关键词： aircraft perception annotation autonomous driving computer vision labeling line identification taxiway data

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LLaFS: When Large Language Models Meet Few-Shot Segmentation

LLaFS: When Large Language Models Meet Few-Shot Segmentation

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Lanyun Chen, Tianrun Ji, Deyi Ye, Jieping Liu, Jun Singapore Univ Technol & Design Singapore Singapore Zhejiang Univ Hangzhou Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350353013;9798350353006

This paper proposes LLaFS, the first attempt to leverage large language models (LLMs) in few-shot segmentation. In contrast to the conventional few-shot segmentation methods that only rely on the limited and biased information from the annotated support images, LLaFS leverages the vast prior knowledge gained by LLM as an effective supplement and directly uses the LLM to segment images in a few-shot manner. To enable the text-based LLM to handle image-related tasks, we carefully design an input instruction that allows the LLM to produce segmentation results represented as polygons, and propose a region-attribute table to simulate the human visual mechanism and provide multi-modal guidance. We also synthesize pseudo samples and use curriculum learning for pretraining to augment data and achieve better optimization. LLaFS achieves state-of-the-art results on multiple datasets, showing the potential of using LLMs for few-shot computer vision tasks.

关键词： Few-shot segmentation Large vision-language models

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SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in vision-Language Models with Counterfactual Examples

SocialCounterfactuals: Probing and Mitigating Intersectional...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Howard, Phillip Madasu, Avinash Le, Tiep Moreno, Gustavo Lujan Bhiwandiwalla, Anahita Lal, Vasudev Intel Labs Santa Clara CA 95052 USA

ISBN: (纸本)9798350353006

While vision-language models (VLMs) have achieved remarkable performance improvements recently, there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge, we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g., a given occupation) while differing only in their depiction of intersectional social attributes (e.g., race & gender). Through our over-generate-then-filter methodology, we produce SocialCounterfactuals, a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender, race, and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs.

关键词： counterfactuals Fairness intersectionality social bias vision-language models

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Spectral and Polarization vision: Spectro-polarimetric Real-world Dataset

Spectral and Polarization Vision: Spectro-polarimetric Real-...

引用

ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Jeon, Yujin Cho, Eunsue Kim, Youngchan Moon, Yunseong Omer, Khalid Heide, Felix Baek, Seung-Hwan POSTECH Pohang South Korea Meta Menlo Pk CA USA Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9798350353006

Image datasets are essential not only in validating existing methods in computer vision but also in developing new methods. Many image datasets exist, consisting of trichromatic intensity images taken with RGB cameras, which are designed to replicate human vision. However, polarization and spectrum, the wave properties of light that animals in harsh environments and with limited brain capacity often rely on, remain underrepresented in existing datasets. Although there are previous spectro-polarimetric datasets, they have insufficient object diversity, limited illumination conditions, linear-only polarization data, and inadequate image count. Here, we introduce two spectro-polarimetric datasets, consisting of trichromatic Stokes images and hyperspectral Stokes images. These datasets encompass both linear and circular polarization;they introduce multiple spectral channels;and they feature a broad selection of real-world scenes. With our dataset in hand, we analyze the spectro-polarimetric image statistics, develop efficient representations of such high-dimensional data, and evaluate spectral dependency of shape-from-polarization methods. As such, the proposed dataset promises a foundation for data-driven spectro-polarimetric imaging and vision research.

关键词： Computational Imaging

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On the Faithfulness of vision Transformer Explanations

On the Faithfulness of Vision Transformer Explanations

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Wu, Junyi Kang, Weitai Tang, Hao Hong, Yuan Yan, Yan IIT Dept Comp Sci Chicago IL 60616 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Univ Connecticut Dept Comp Sci Storrs CT USA

ISBN: (纸本)9798350353006

To interpret vision Transformers, post-hoc explanations assign salience scores to input pixels, providing human-understandable heatmaps. However, whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap, we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness, we introduce Salience-guided Faithfulness Coefficient (SaCo), a novel evaluation metric leveraging essential information of salience distribution. Specifically, we con-duct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores, resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution, thereby failing to capture the faithfulness property. In contrast, our pro-posed SaCo offers a reliable faithfulness measurement, establishing a robust metric for interpretations. Furthermore, our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation, shedding light on potential paths for advancing vision Transformer explainability.

关键词： Explainability Transformer

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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...

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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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Action Scene Graphs for Long-Form Understanding of Egocentric Videos

Action Scene Graphs for Long-Form Understanding of Egocentri...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Rodin, Ivan Furnari, Antonino Min, Kyle Tripathi, Subarna Farinella, Giovanni Maria Univ Catania Catania Italy Intel Labs Hillsboro OR USA

ISBN: (纸本)9798350353006

We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos, such as verb-noun action labels, by providing a temporally evolving graph-based description of the actions performed by the camera wearer, including interacted objects, their relationships, and how actions unfold in time. Through a novel annotation procedure, we extend the Ego4D dataset adding manually labeled Egocentric Action Scene Graphs which offer a rich set of annotations for long-from egocentric video understanding. We hence define the EASG generation task and provide a baseline approach, establishing preliminary benchmarks. Experiments on two downstream tasks, action anticipation and activity summarization, highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and code to replicate experiments and annotations 1 1 The code is available at https://***/fpv-iplab/EASG.

关键词： egocentric vision long-form video understanding scene graphs

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A Comprehensive Analysis of Factors Impacting Membership Inference

A Comprehensive Analysis of Factors Impacting Membership Inf...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： DeAlcala, Daniel Mancera, Gonzalo Morales, Aythami Fierrez, Julian Tolosana, Ruben Ortega-Garcia, Javier Univ Autonoma Madrid Biometr & Data Pattern Analyt Lab Madrid Spain

ISBN: (纸本)9798350365474

We analyze various factors affecting the proper functioning of MIA and MINT, two research lines aimed at detecting data used for training. The difference between these lines lies in the environmental conditions, while the fundamental bases are similar for both. As evident in the literature, this detection task is far from straightforward and poses an ongoing challenge for the scientific community. Specifically, in this work, we conclude that factors such as the number of times data passes through the original network, the loss function, or dropout significantly impact detection outcomes. Therefore, it is crucial to consider them when developing these methods and during the training of any neural network, both to avoid (MIA) and to enhance (MINT) this detection. We evaluate the AdaFace facial recognition model using five databases with over 22 million images, modifying the different factors under analysis and defining a suitable protocol for their examination. State-of-the-art accuracy reaching up to 87% is achieved, surpassing existing methods.

关键词： Face recognition Fairness Membership Inference MIA MINT Realiability

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Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models

Generative Rendering: Controllable 4D-Guided Video Generatio...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Cai, Shengqu Ceylan, Duygu Gadelha, Matheus Huang, Chun-Hao Paul Wang, Tuanfeng Yang Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA Adobe Res San Francisco CA USA

ISBN: (纸本)9798350353013;9798350353006

Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry, appearance, motion, and camera path. Creating computer-generated videos, however, is a tedious manual process, which can be automated by emerging text-to-video diffusion models. Despite great promise, video diffusion models are difficult to control, hindering a user to apply their own creativity rather than amplifying it. To address this challenge, we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose, our approach takes an animated, low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path. Project page: ***/generative_rendering.

关键词： Animation computer Graphics computer vision Generative Models Video Generation Video Synthesis

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