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Enhancing vision-Language Few-Shot Adaptation with Negative Learning

Enhancing Vision-Language Few-Shot Adaptation with Negative ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Ce Zhang Simon Stepputtis Katia Sycara Yaqi Xie School of Computer Science Carnegie Mellon University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Large-scale pre-trained vision-Language Models (VLMs) have exhibited impressive zero-shot performance and transferability, allowing them to adapt to downstream tasks in a data-efficient manner. However, when only a few labeled samples are available, adapting VLMs to distinguish subtle differences between similar classes in specific downstream tasks remains challenging. In this work, we propose a Simple yet effective Negative Learning approach, SimNL, to more efficiently exploit the task-specific knowledge from few-shot labeled samples. Unlike previous methods that focus on identifying a set of representative positive features defining “what is a {CLASS} ”, SimNL discovers a complementary set of negative features that define “what is not a {CLASS}”, providing additional insights that supplement the positive features to enhance task-specific recognition capability. Further, we identify that current adaptation approaches are particularly vulnerable to potential noise in the fewshot sample set. To mitigate this issue, we introduce a plug-and-play few-shot instance reweighting technique to suppress noisy outliers and amplify clean samples for more stable adaptation. Our extensive experimental results across 15 datasets validate that the proposed SimNL outperforms existing state-of-the-art methods on both few-shot learning and domain generalization tasks while achieving competitive computational efficiency. Code is available at https://***/zhangce01/SimNL.

关键词： Adaptation models computer vision Codes Accuracy Computational modeling Noise Transforms Computational efficiency Noise measurement Few shot learning

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Re-identifying People in Video via Learned Temporal Attention and Multi-modal Foundation Models

Re-identifying People in Video via Learned Temporal Attentio...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Cole Hill Florence Yellin Krishna Regmi Dawei Du Scott McCloskey Kitware Inc. USA University of South Florida USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Biometric recognition from security camera video is a challenging problem when the individuals change clothes or when they are partly occluded. Others have recently demonstrated that CLIP's visual encoder performs well in this domain, but existing methods fail to make use of the model's text encoder or temporal information available in video. In this paper, we present VCLIP, a method for person identification in videos captured in challenging poses and with changes to a person's clothing. Harnessing the power of pre-trained vision-language models, we Jointly train a temporal fusion network while fine-tuning the visual encoder. To leverage the cross-modal embedding space, we use learned biometric pedestrian attribute features to further enhance our model's person re-identification (Re-ID) ability. We demonstrate significant performance improvements via experiments with the MEVID and CCVID datasets, particularly in the more challenging clothes-changing conditions. In support of this and future methods that use textual attributes for Re-ID with multimodal models, we release a dataset of annotated pedestrian attributes for the popular MEVID dataset [4].

关键词： Biometrics Visualization computer vision Pedestrians Foundation models Biological system modeling Clothing Cameras Robustness Security

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Multi-Level Feature Distillation of Joint Teachers Trained on Distinct Image Datasets

Multi-Level Feature Distillation of Joint Teachers Trained o...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Adrian Iordache Bogdan Alexe Radu Tudor Ionescu Department of Computer Science University of Bucharest Bucharest Romania “Gheorghe Mihoc - Caius Iacob” Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset. Then, the teachers are combined into a joint architecture, which fuses the features of all teachers at multiple representation levels. The joint teacher architecture is fine-tuned on samples from all datasets, thus gathering useful generic information from all data samples. Finally, we employ a multi-level feature distillation procedure to transfer the knowledge to a student model for each of the considered datasets. We conduct image classification experiments on seven benchmarks, and action recognition experiments on three benchmarks. To illustrate the power of our feature distillation procedure, the student architectures are chosen to be identical to those of the individual teachers. To demonstrate the flexibility of our approach, we combine teachers with distinct architectures. We show that our novel Multi-Level Feature Distillation (MLFD) can significantly surpass equivalent architectures that are either trained on individual datasets, or jointly trained on all datasets at once. Furthermore, we confirm that each step of the proposed training procedure is well motivated by a comprehensive ablation study. We publicly release our code at https://***/AdrianIordache/MLFD.

关键词： Training Convolutional codes Image segmentation Image recognition computer architecture Object detection Benchmark testing Transformers Natural language processing Image classification

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Recurrence-Based Vanishing Point Detection

Recurrence-Based Vanishing Point Detection

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Skanda Bharadwaj Robert T. Collins Yanxi Liu School of EECS The Pennsylvania State University University Park PA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Classical approaches to Vanishing Point Detection (VPD) rely solely on the presence of explicit straight lines in images, while recent supervised deep learning approaches need labeled datasets for training. We propose an alternative unsupervised approach: Recurrence-based Vanishing Point Detection (R-VPD) that uses implicit lines discovered from recurring correspondences in addition to explicit lines. Furthermore, we contribute two Recurring-pattern-for-Vanishing-Point (RPVP) datasets: 1) a Synthetic Image dataset with 3,200 ground truth vanishing points and camera parameters, and 2) a Real-World Image dataset with 1,400 human annotated vanishing points. We compare our method with two classical methods and two state-of-the-art deep learning-based VPD methods. We demonstrate that our unsupervised approach outperforms all the methods on the synthetic images dataset, outperforms the classical methods, and is on par with the supervised learning approaches on real-world images. Code and data can be found here: http://***/data/***

关键词： Deep learning Training computer vision Codes Urban areas Supervised learning Cameras Feature extraction Detection algorithms Unsupervised learning

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ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization

ARF-Plus: Controlling Perceptual Factors in Artistic Radianc...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Wenzhao Li Tianhao Wu Fangcheng Zhong Cengiz Oztireli University of Cambridge

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The radiance fields style transfer is an emerging field that has recently gained popularity as a means of 3D scene stylization, thanks to the outstanding performance of neural radiance fields in 3D reconstruction and view synthesis. We highlight a research gap in radiance fields style transfer, the need for sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer. In this paper, we present ARF-Plus, a unique 3D neural style transfer framework offering manageable control over perceptual factors, to systematically explore the perceptual controllability in 3D scene stylization. Four distinct types of controls - color preservation control, (style pattern) scale control, spatial (selective stylization area) control, and depth enhancement control - come with our proposed novel loss functions and strategies, seamlessly integrated into this framework. This unlocks a realm of limitless possibilities, allowing customized modifications of stylization effects and flexible merging of the strengths of different styles, ultimately enabling the creation of novel and eye-catching stylistic effects on 3D scenes.

关键词： computer vision Three-dimensional displays Image color analysis Merging Controllability Neural radiance field Neural style transfer

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RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity RAW Image Generation

RAW-Diffusion: RGB-Guided Diffusion Models for High-Fidelity...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Christoph Reinders Radu Berdan Beril Besbinar Junji Otsuka Daisuke Iso Leibniz University Hannover Sony AI Sony Group Corporation

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Current deep learning approaches in computer vision primarily focus on RGB data sacrificing information. In contrast, RAW images offer richer representation, which is crucial for precise recognition, particularly in challenging conditions like low-light environments. The resultant demand for comprehensive RAW image datasets contrasts with the labor-intensive process of creating specific datasets for individual sensors. To address this, we propose a novel diffusion-based method for generating RAW images guided by RGB images. Our approach integrates an RGB-guidance module for feature extraction from RGB inputs, then incorporates these features into the reverse diffusion process with RGB-guided residual blocks across various resolutions. This approach yields high-fidelity RAW images, enabling the creation of camera-specific RAW datasets. Our RGB2RAW experiments on four DSLR datasets demonstrate state-of-the-art performance. Moreover, RAW-Diffusion demonstrates exceptional data efficiency, achieving remarkable performance with as few as 25 training samples or even fewer. We extend our method to create BDD100K-RAW and Cityscapes-RAW datasets, revealing its effectiveness for object detection in RAW imagery, significantly reducing the amount of required RAW images. The code is available at https://***/SonyResearch/RAW-Diffusion.

关键词： Training Image sensors computer vision Image synthesis Noise reduction Object detection Feature extraction Cameras Sensors Iterative methods

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MADation: Face Morphing Attack Detection with Foundation Models

MADation: Face Morphing Attack Detection with Foundation Mod...

引用

ieee Winter Applications and computer vision Workshops (WACVW)

作者： Eduarda Caldeira Guray Ozgur Tahar Chettaoui Marija Ivanovska Peter Peer Fadi Boutros Vitomir Struc Naser Damer Fraunhofer IGD Germany University of Ljubljana Slovenia TU Darmstadt Germany

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

Despite the considerable performance improvements of face recognition algorithms in recent years, the same scientific advances responsible for this progress can also be used to create efficient ways to attack them, posing a threat to their secure deployment. Morphing attack detection (MAD) systems aim to detect a specific type of threat, morphing attacks, at an early stage, preventing them from being considered for verification in critical processes. Foundation models (FM) learn from extensive amounts of unlabelled data, achieving remarkable zero-shot generalization to unseen domains. Although this generalization capacity might be weak when dealing with domain-specific downstream tasks such as MAD, FMs can easily adapt to these settings while retaining the built-in knowledge acquired during pretraining. In this work, we recognize the potential of FMs to perform well in the MAD task when properly adapted to its specificities. To this end, we adapt FM CLIP architectures with LoRA weights while simultaneously training a classification header. The proposed framework, MADation surpasses our alternative FM and transformer-based frameworks and constitutes the first adaption of FMs to the MAD task. MADation presents competitive results with current MAD solutions in the literature and even surpasses them in several evaluation scenarios. To encourage reproducibility and facilitate further research in MAD, we publicly release the implementation of MADation at https://***/gurayozgur/MADation.

关键词： Training computer vision Frequency modulation Foundation models Face recognition conferences Training data Benchmark testing Transformers Reproducibility of results

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Learning to Count from Pseudo-Labeled Segmentation

Learning to Count from Pseudo-Labeled Segmentation

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ieee Workshop on Applications of computer vision (WACV)

作者： Jingyi Xu Hieu Le Dimitris Samaras Stony Brook University EPFL

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Class-agnostic counting (CAC) has numerous potential applications across various domains. The goal is to count objects of an arbitrary category during testing, based on only a few annotated exemplars. However, existing methods often count all objects in the image, including those from different categories than the exemplars. To address this issue, we propose localizing the area containing the objects of interest via an exemplar-based segmentation model before counting them. To train this model, we propose a novel method to obtain pseudo-labeled segmentation masks. Specifically, we use an unsupervised image clustering method to generate a set of candidate pseudo object masks, from which we select the optimal one using a pretrained CAC model. We show that the trained segmentation model can effectively localize objects of interest based on the exemplars and prevent the model from counting everything. To properly evaluate the performance of CAC methods in real-world scenarios, we introduce two new benchmarks: a synthetic test set and a new test set of real images containing countable objects from multiple classes. Our proposed method shows a significant advantage over previous CAC methods on these two benchmarks.

关键词： Training Image segmentation computer vision Annotations Computational modeling Clustering methods Benchmark testing Data models Object recognition Synthetic data

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Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmentation

Class-Agnostic Visio-Temporal Scene Sketch Semantic Segmenta...

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ieee Workshop on Applications of computer vision (WACV)

作者： Aleyna Kütük Tevfik Metin Sezgin KUIS AI Center Koç University İstanbul Turkey

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Scene sketch semantic segmentation is a crucial task for various applications including sketch-to-image retrieval and scene understanding. Existing sketch segmentation methods treat sketches as bitmap images, leading to the loss of temporal order among strokes due to the shift from vector to image format. Moreover, these methods struggle to segment objects from categories absent in the training data. In this paper, we propose a Class-Agnostic Visio-Temporal Network (CAVT) for scene sketch semantic segmentation. CAVT employs a class-agnostic object detector to detect individual objects in a scene and groups the strokes of instances through its post-processing module. This is the first approach that performs segmentation at both the instance and stroke levels within scene sketches. Furthermore, there is a lack of free-hand scene sketch datasets with both instance and stroke-level class annotations. To fill this gap, we collected the largest Free-hand Instance-and Stroke-level Scene Sketch Dataset (FrISS) that contains 1K scene sketches and covers 403 object classes with dense annotations. Extensive experiments on FrISS and other datasets demonstrate the superior performance of our method over state-of-the-art scene sketch segmenttion models. Our code and dataset can be accessed from https://***/aleynakutuk6/CAVT.

关键词： Visualization computer vision Codes Annotations Semantic segmentation Pipelines Training data Detectors Vectors Object recognition

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Mixed Patch Visible-Infrared Modality Agnostic Object Detection

Mixed Patch Visible-Infrared Modality Agnostic Object Detect...

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ieee Workshop on Applications of computer vision (WACV)

作者： Heitor R. Medeiros David Latortue Eric Granger Marco Pedersoli Dept. of Systems Engineering Laboratoire d'imagerie de vision et d'intelligence artificielle (LIVIA) International Laboratory on Learning Systems (ILLS) ETS Montreal Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

In real-world scenarios, using multiple modalities like visible (RGB) and infrared (IR) can greatly improve the performance of a predictive task such as object detection (OD). Multimodal learning is a common way to leverage these modalities, where multiple modality-specific encoders and a fusion module are used to improve performance. In this paper, we tackle a different way to employ RGB and IR modalities, where only one modality or the other is observed by a single shared vision encoder. This realistic setting requires a lower memory footprint and is more suitable for applications such as autonomous driving and surveillance, which commonly rely on RGB and IR data. However, when learning a single encoder on multiple modalities, one modality can dominate the other, producing un-even recognition results. This work investigates how to efficiently leverage RGB and IR modalities to train a common transformer-based OD vision encoder while countering the effects of modality imbalance. For this, we introduce a novel training technique to Mix Patches (MiPa)from the two modalities, in conjunction with a patch-wise modality agnostic module, for learning a common representation of both modalities. Our experiments show that MiPa can learn a representation to reach competitive results on traditional RGB/IR benchmarks while only requiring a single modality during inference. Our code is available at: https://***/heitorrapela/MiPa.

关键词： Training computer vision Codes Surveillance Memory management Object detection Detectors Transformers Encoding Mutual information

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