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Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Good at captioning, bad at counting: Benchmarking GPT-4V on ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chenhui Zhang Sherrie Wang Institute for Data Systems and Society Massachusetts Institute of Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Large vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear to what extent capabilities on natural images transfer to Earth observation (EO) data, which are predominantly satellite and aerial images less common in VLM training data. In this work, we propose a comprehensive benchmark to gauge the progress of VLMs toward being useful tools for EO data by assessing their abilities on scene understanding, localization and counting, and change detection. Motivated by real-world applications, our benchmark includes scenarios like urban monitoring, disaster relief, land use, and conservation. We discover that, although state-of-the-art VLMs like GPT-4V possess world knowledge that leads to strong performance on location understanding and image captioning, their poor spatial reasoning limits usefulness on object localization and counting. Our benchmark is publicly available on this website. A full version of this paper can be found here.

关键词： Location awareness Earth Visualization Satellites Natural languages Training data Land surface

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Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Galadrielle Humblot-Renaux Sergio Escalera Thomas B. Moeslund Visual Analysis and Perception Lab Aalborg University Denmark Computer Vision Center Universitat Autònoma de Barcelona Spain Dept. of Mathematics and Informatics Universitat de Barcelona Spain

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc

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Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis

Unveiling the Ambiguity in Neural Inverse Rendering: A Param...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Georgios Kouros Minye Wu Sushruth Nagesh Xianling Zhang Tinne Tuytelaars KU Leuven Ford Motor Company

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images. However, it is an ill-posed problem prone to producing ambiguous estimations deviating from physically accurate representations. In this paper, we utilize Neural Microfacet Fields (NMF), a state-of-the-art neural inverse rendering method to illustrate the inherent ambiguity. We propose an evaluation framework to assess the degree of compensation or interaction between the estimated scene properties, aiming to explore the mechanisms behind this ill-posed problem and potential mitigation strategies. Specifically, we introduce artificial perturbations to one scene property and examine how adjusting another property can compensate for these perturbations. To facilitate such experiments, we introduce a disentangled NMF where material properties are independent. The experimental findings underscore the intrinsic ambiguity present in neural inverse rendering and highlight the importance of providing additional guidance through geometry, material, and illumination priors.

关键词： Geometry Head Perturbation methods Lighting Rendering (computer graphics) Neural microtechnology Safety

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All Keypoints You Need: Detecting Arbitrary Keypoints on the Body of Triple, High, and Long Jump Athletes

All Keypoints You Need: Detecting Arbitrary Keypoints on the...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Katja Ludwig Julian Lorenz Robin Schön Rainer Lienhart Chair for Machine Learning and Computer Vision University of Augsburg

Performance analyses based on videos are commonly used by coaches of athletes in various sports disciplines. In individual sports, these analyses mainly comprise the body posture. This paper focuses on the disciplines of triple, high, and long jump, which require fine-grained locations of the athlete’s body. Typical human pose estimation datasets provide only a very limited set of keypoints, which is not sufficient in this case. Therefore, we propose a method to detect arbitrary keypoints on the whole body of the athlete by leveraging the limited set of annotated keypoints and auto-generated segmentation masks of body parts. Evaluations show that our model is capable of detecting keypoints on the head, torso, hands, feet, arms, and legs, including also bent elbows and knees. We analyze and compare different techniques to encode desired keypoints as the model’s input and their embedding for the Transformer backbone.

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Density Map Distillation for Incremental Object Counting

Density Map Distillation for Incremental Object Counting

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chenshen Wu Joost van de Weijer Computer Vision Center Barcelona Spain

We investigate the problem of incremental learning for object counting, where a method must learn to count a variety of object classes from a sequence of datasets. A naïve approach to incremental object counting would suffer from catastrophic forgetting, where it would suffer from a dramatic performance drop on previous tasks. In this paper, we propose a new exemplar-free functional regularization method, called Density Map Distillation (DMD). During training, we introduce a new counter head for each task and introduce a distillation loss to prevent forgetting of previous tasks. Additionally, we introduce a cross-task adaptor that projects the features of the current backbone to the previous backbone. This projector allows for the learning of new features while the backbone retains the relevant features for previous tasks. Finally, we set up experiments of incremental learning for counting new objects. Results confirm that our method greatly reduces catastrophic forgetting and outperforms existing methods.

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Low-Rank Few-Shot Adaptation of vision-Language Models

Low-Rank Few-Shot Adaptation of Vision-Language Models

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Maxime Zanella Ismail Ben Ayed UCLouvain UMons ÉTS Montréal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent progress in the few-shot adaptation of visionLanguage Models (VLMs) has further pushed their generalization capabilities, at the expense of just a few labeled samples within the target downstream task. However, this promising, already quite abundant few-shot literature has focused principally on prompt learning and, to a lesser extent, on adapters, overlooking the recent advances in Parameter-Efficient Fine-Tuning (PEFT). Furthermore, existing few-shot learning methods for VLMs often rely on heavy training procedures and/or carefully chosen, taskspecific hyper-parameters, which might impede their applicability. In response, we introduce Low-Rank Adaptation (LoRA) in few-shot learning for VLMs, and show its potential on 11 datasets, in comparison to current state-of-the-art prompt- and adapter-based approaches. Surprisingly, our simple CLIP-LoRA method exhibits substantial improvements, while reducing the training times and keeping the same hyper-parameters in all the target tasks, i.e., across all the datasets and numbers of shots. Certainly, our surprising results do not dismiss the potential of promptlearning and adapter-based research. However, we believe that our strong baseline could be used to evaluate progress in these emergent subjects in few-shot VLMs.

关键词： Training Adaptation models computer vision Design methodology conferences pattern recognition Few shot learning

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Emotic Masked Autoencoder on Dual-views with Attention Fusion for Facial Expression recognition

Emotic Masked Autoencoder on Dual-views with Attention Fusio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xuan-Bach Nguyen Hoang-Thien Nguyen Thanh-Huy Nguyen Nhu-Tai Do Quang Vinh Dinh University of Technology Ho Chi Minh City Vietnam Posts and Telecommunications Institute of Technology Ho Chi Minh City Vietnam University of Education Ho Chi Minh City Vietnam University of Economics Ho Chi Minh City-UEH Vietnam Vietnamese-German University Vietnam

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Facial Expression recognition (FER) is a critical task within computer vision with diverse applications across various domains. Addressing the challenge of limited FER datasets, which hampers the generalization capability of expression recognition models, is imperative for enhancing performance. Our paper presents an innovative approach integrating the MAE-Face self-supervised learning (SSL) method and multi-view Fusion Attention mechanism for expression classification, particularly showcased in the 6th Affective Behavior Analysis in-the-wild (ABAW) competition. By utilizing low-level feature information from the ipsilateral view (auxiliary view) before learning the high-level feature that emphasizes the shift in the human facial expression, our work seeks to provide a straightforward yet innovative way to improve the examined view (main view). We also suggest easy-to-implement and no-training frameworks aimed at highlighting key facial features to determine if such features can serve as guides for the model, focusing on pivotal local elements. The efficacy of this method is validated by improvements in model performance on the Aff-wild2 dataset, as observed in both training and validation contexts.

关键词： Training computer vision Attention mechanisms Computational modeling Face recognition Refining Focusing

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MMA-DFER: MultiModal Adaptation of unimodal models for Dynamic Facial Expression recognition in-the-wild

MMA-DFER: MultiModal Adaptation of unimodal models for Dynam...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Kateryna Chumachenko Alexandros Iosifidis Moncef Gabbouj Tampere University Tampere Finland Aarhus University Aarhus Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dynamic Facial Expression recognition (DFER) has received significant interest in the recent years dictated by its pivotal role in enabling empathic and human-compatible technologies. Achieving robustness towards in-the-wild data in DFER is particularly important for real-world applications. One of the directions aimed at improving such models is multimodal emotion recognition based on audio and video data. Multimodal learning in DFER increases the model capabilities by leveraging richer, complementary data representations. Within the field of multimodal DFER, recent methods have focused on exploiting advances of self-supervised learning (SSL) for pre-training of strong multi-modal encoders [40]. Another line of research has focused on adapting pre-trained static models for DFER [8]. In this work, we propose a different perspective on the problem and investigate the advancement of multimodal DFER performance by adapting SSL-pre-trained disjoint unimodal encoders. We identify main challenges associated with this task, namely, intra-modality adaptation, cross-modal alignment, and temporal adaptation, and propose solutions to each of them. As a result, we demonstrate improvement over current state-of-the-art on two popular DFER benchmarks, namely DFEW [19] and MFAW [29].

关键词： Adaptation models Emotion recognition computer vision Face recognition conferences Computational modeling Self-supervised learning

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UP-NAS: Unified Proxy for Neural Architecture Search

UP-NAS: Unified Proxy for Neural Architecture Search

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yi-Cheng Huang Wei-Hua Li Chih-Han Tsou Jun-Cheng Chen Chu-Song Chen National Taiwan University Academia Sinica

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recently, zero-cost proxies for neural architecture search (NAS) have attracted increasing attention. They allow us to discover top-performing neural networks through architecture scoring without requiring training a very large network (i.e., supernet). Thus, it can save significant computation resources to complete the search. However, to our knowledge, no single proxy works best for different tasks and scenarios. To consolidate the strength of different proxies and to reduce search bias, we propose a unified proxy neural architecture search framework (UP-NAS) which learns a multi-proxy estimator for predicting a unified score by combining multiple zero-cost proxies. The predicted score is then used for an efficient gradient-ascent architecture search in the embedding space of the neural network architectures. Our approach can not only save computational time required for multiple proxies during architecture search but also gain the flexibility to consolidate the existing proxies on different tasks. We conduct experiments on the search spaces of NAS-Bench-201 and DARTS in different datasets. The results demonstrate the effectiveness of the proposed approach. Code is available at https://***/AI-Application-andIntegration-Lab/UP-NAS.

关键词： Training computer vision Codes conferences Neural networks computer architecture pattern recognition

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Unlocking the Full Potential of Small Data with Diverse Supervision

Unlocking the Full Potential of Small Data with Diverse Supe...

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

作者： Pang, Ziqi Hu, Zhiyuan Tokmakov, Pavel Wang, Yu-Xiong Hebert, Martial TuSimple Beijing Peoples R China Univ Calif San Diego San Diego CA USA Toyota Res Inst Ann Arbor MI USA UIUC Champaign IL USA CMU Pittsburgh PA USA

ISBN: (纸本)9781665448994

Virtually all of deep learning literature relies on the assumption of large amounts of available training data. Indeed, even the majority of few-shot learning methods rely on a large set of "base classes" for pre-training. This assumption, however, does not always hold. For some tasks, annotating a large number of classes can be infeasible, and even collecting the images themselves can be a challenge in some scenarios. In this paper, we study this problem and call it "Small Data" setting, in contrast to "Big Data." To unlock the full potential of small data, we propose to augment the models with annotations for other related tasks, thus increasing their generalization abilities. In particular, we use the richly annotated scene parsing dataset ADE20K to construct our realistic Long-tail recognition with Diverse Supervision (LRDS) benchmark, by splitting the object categories into head and tail based on their distribution. Following the standard few-shot learning protocol, we use the head classes for representation learning and the tail classes for evaluation. Moreover, we further subsample the head categories and images to generate two novel settings which we call "Scarce-Class" and "Scarce-Image," respectively corresponding to the shortage of training classes and images. Finally, we analyze the effect of applying various additional supervision sources under the proposed settings. Our experiments demonstrate that densely labeling a small set of images can indeed largely remedy the small data constraints. Our code and benchmark are available at https://***/BinahHu/ADE-FewShot.

关键词： Training Learning systems Head Protocols Training data Benchmark testing pattern recognition

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