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Prior2Posterior: Model Prior Correction for Long-Tailed Learning

Prior2Posterior: Model Prior Correction for Long-Tailed Lear...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： S Divakar Bhat Amit More Mudit Soni Surbhi Agrawal Honda R&D Co. Ltd. Tokyo Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Learning-based solutions for long-tailed recognition face difficulties in generalizing on balanced test datasets. Due to imbalanced data prior, the learned a posteriori distribution is biased toward the most frequent (head) classes, leading to an inferior performance on the least frequent (tail) classes. In general, the performance can be improved by removing such a bias by eliminating the effect of imbalanced prior modeled using the number of class samples (frequencies). We first observe that the effective prior on the classes, learned by the model at the end of the training, can differ from the empirical prior obtained using class frequencies. Thus, we propose a novel approach to accurately model the effective prior of a trained model using a posteriori probabilities. We propose to correct the imbalanced prior by adjusting the predicted a posteriori probabilities (Prior2Posterior: P2P) using the calculated prior in a post-hoc manner after the training, and show that it can result in improved model performance. We present theoretical analysis showing the optimality of our approach for models trained with naive cross-entropy loss as well as logit adjusted loss. Our experiments show that the proposed approach achieves new state-of-the-art (SOTA) on several benchmark datasets from the long-tail literature in the category of logit adjustment methods. Further, the proposed approach can be used to inspect any existing method to capture the effective prior and remove any residual bias to improve its performance, post-hoc, without model retraining. We also show that by using the proposed post-hoc approach, the performance of many existing methods can be improved further.

关键词： Training computer vision Analytical models Heavily-tailed distribution Face recognition Tail Probability Predictive models Benchmark testing

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Spatio-Temporal Context Prompting for Zero-Shot Action Detection

Spatio-Temporal Context Prompting for Zero-Shot Action Detec...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Wei-Jhe Huang Min-Hung Chen Shang-Hong Lai National Tsing Hua University Taiwan NVIDIA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Spatio-temporal action detection encompasses the tasks of localizing and classifying individual actions within a video. Recent works aim to enhance this process by incorporating interaction modeling, which captures the relationship between people and their surrounding context. However, these approaches have primarily focused on fully-supervised learning, and the current limitation lies in the lack of generalization capability to recognize unseen action categories. In this paper, we aim to adapt the pretrained image-language models to detect unseen actions. To this end, we propose a method which can effectively leverage the rich knowledge of visual-language models to perform Person-Context Interaction. Meanwhile, our Context Prompting module will utilize contextual information to prompt labels, thereby enhancing the generation of more representative text features. Moreover, to address the challenge of recognizing distinct actions by multiple people at the same timestamp, we design the Interest Token Spotting mechanism which employs pretrained visual knowledge to find each person's interest context tokens, and then these tokens will be used for prompting to generate text features tailored to each individual. To evaluate the ability to detect unseen actions, we propose a comprehensive benchmark on J-HMDB, UCF101-24, and AVA datasets. The experiments show that our method achieves superior results compared to previous approaches and can be further extended to multi-action videos, bringing it closer to real-world applications. The code and data can be found in ST-CLIP.

关键词： Adaptation models Visualization computer vision Codes Text recognition Benchmark testing Context modeling

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DiL: An Explainable and Practical Metric for Abnormal Uncertainty in Object Detection

DiL: An Explainable and Practical Metric for Abnormal Uncert...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Amit Giloni Omer Hofman Ikuya Morikawa Toshiya Shimizu Yuval Elovici Asaf Shabtai Ben-Gurion University of the Negev Fujitsu Research of Europe Fujitsu Limited

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Although object detection models are widely used, their predictive performance has been shown to deteriorate when faced with abnormal scenes. Such abnormalities can occur naturally (by partially occluded or out-of-distribution objects) or deliberately (in the case of an adversarial attack). Existing uncertainty quantification methods, such as object detection evaluation metrics and label-uncertainty quantification techniques, do not consider the abnormalities' effect on the model's internal decision-making process. Furthermore, practical methods that consider the effects of abnormalities (such as abnormality detection and mitigation) are designed to deal with one type of abnormality. We present distinctive localization (DiL), an unsupervised, practical and explainable metric that quantitatively interprets any type of abnormality and can be leveraged for preventive purposes. By utilizing XAI techniques (saliency maps), DiL maps the objectness of a given scene and captures the model's inner uncertainty regarding the identified (and missed) objects. DiL was evaluated across nine use cases, including partially occluded and out-of-distribution objects, as well as adversarial patches, in both physical and digital spaces, on benchmark datasets, and our newly E-PO dataset (generated with DALL-E 2). Our results show that DiL: i) successfully interprets and quantifies an abnormality's effect on the model's decision-making process, regardless of the abnormality type; and ii) can be leveraged to detect and mitigate this effect.

关键词： Measurement Location awareness computer vision Uncertainty Prevention and mitigation Computational modeling Decision making Object detection Predictive models Object recognition

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Loose Social-Interaction recognition in Real-World Therapy Scenarios

Loose Social-Interaction Recognition in Real-World Therapy S...

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ieee Workshop on Applications of computer vision (WACV)

作者： Abid Ali Rui Dai Ashish Marisetty Guillaume Astruc Monique Thonnat Jean-Marc Odobez Susanne Thümmler Francois Bremond INRIA University Cote d’Azur Idiap

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The computer vision community has explored dyadic interactions for atomic actions such as pushing, carrying-object, etc. However, with the advancement in deep learning models, there is a need to explore more complex dyadic situations such as loose interactions. These are interactions where two people perform certain atomic activities to complete a global action irrespective of temporal synchronisation and physical engagement, like cooking-together for example. Analysing these types of dyadic-interactions has several useful applications in the medical domain for social-skills development and mental health diagnosis. To achieve this, we propose a novel dual-path architecture to capture the loose interaction between two individuals. Our model learns global abstract features from each stream via a CNNs backbone and fuses them using a new Global-Layer-Attention module based on a cross-attention strategy. We evaluate our model on real-world autism diagnoses such as our Loose-Interaction dataset, and the publicly available Autism dataset for loose interactions. Our network achieves baseline results on the Loose-Interaction and SOTA results on the Autism datasets. Moreover, we study different social interactions by experimenting on a publicly available dataset i.e. NTU-RGB+D (interactive classes from both NTU-60 and NTU-120). We have found that different interactions require different network designs. We also compare a slightly different version of our method (details in Section 3.6) by incorporating time information to address tight interactions achieving SOTA results.

关键词： Deep learning Autism computer vision Fuses Computational modeling Medical treatment Mental health computer architecture Synchronization Medical diagnostic imaging

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Data-Efficient 3D Visual Grounding via Order-Aware Referring

Data-Efficient 3D Visual Grounding via Order-Aware Referring

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Tung-Yu Wu Sheng-Yu Huang Yu-Chiang Frank Wang Department of Electrical Engineering National Taiwan University NVIDIA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

3D visual grounding aims to identify the target object within a 3D point cloud scene referred to by a natural language description. Previous works usually require significant data relating to point color and their descriptions to exploit the corresponding complicated verbo-visual relations. In our work, we introduce Vigor, a novel Data-Efficient 3D Visual Grounding framework via Order-aware Referring. Vigor leverages LLM to produce a desirable referential order from the input description for 3D visual grounding. With the proposed stacked object-referring blocks, the predicted anchor objects in the above order allow one to locate the target object progressively with-out supervision on the identities of anchor objects or exact relations between anchor/target objects. We also present an order-aware warm-up training strategy, which augments referential orders for pre-training the visual grounding framework, allowing us to better capture the complex verbo-visual relations and benefit the desirable data-efficient learning scheme. Experimental results on the NR3D and ScanRefer datasets demonstrate our superiority in low-resource scenarios. In particular, Vigor surpasses current state-of-the-art frameworks by 9.3% and 7.6% grounding accuracy under 1% data and 10% data settings on the NR3D dataset, respectively.

关键词： Training Point cloud compression Visualization computer vision Three-dimensional displays Grounding Image color analysis Natural languages Benchmark testing Object recognition

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Learning Multiple Object States from Actions via Large Language Models

Learning Multiple Object States from Actions via Large Langu...

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ieee Workshop on Applications of computer vision (WACV)

作者： Masatoshi Tateno Takuma Yagi Ryosuke Furuta Yoichi Sato The University of Tokyo National Institute of Advanced Industrial Science and Technology (AIST)

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Recognizing the states of objects in a video is crucial in understanding the scene beyond actions and objects. For instance, an egg can be raw, cracked, and whisked while cooking an omelet, and these states can coexist simulta-neously (an egg can be both raw and whisked). However, most existing research assumes single object state change (e.g. uncracked → cracked), overlooking the coexisting nature of multiple object states and the influence of past states on the current state. We formulate object state recognition as a multi-label classification task that explicitly handles multiple states. We then propose to learn multiple object states from narrated videos by leveraging LLMs to generate pseudo-labels from the transcribed narrations, capturing the influence of past states. The challenge is that narrations mostly describe human actions in the video but rarely explain object states. Therefore, we use LLM's knowledge of the relationship between actions and states to derive the missing object states. We further accumulate the derived object states to consider the past state contexts to infer current object state pseudo-labels. We newly collect Multiple Object States Transition (MOST) dataset, which includes manual multi-label annotation for evaluation purposes, covering 60 object states across six object categories. Experimental results show that our model trained on LLM-generated pseudo-labels significantly outperforms strong vision-language models, demonstrating the effectiveness of our pseudo-labeling framework that considers past context via LLMs.

关键词： computer vision Analytical models Accuracy Annotations Computational modeling Large language models Catalysts Multi label classification Manuals Context modeling

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

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ieee Workshop on Applications of computer vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： computer vision Visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction Biomedical imaging Visual perception

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A Regional-Level Resource-Saving Model for Winter Road Surface Snow Detection in Extreme Weathers

A Regional-Level Resource-Saving Model for Winter Road Surfa...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Xinhao Zhou Tong Wang Zhaodong Liu Hao Wei Guangyuan Pan School of Automation and Electrical Engineering Linyi University Shandong China Department of Civil and Environmental Engineering University of Waterloo Waterloo Canada

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Achieving timely and accurate snow detection on road surfaces in extreme weather conditions is vital for both transportation and computer vision applications. However, conventional object detection models, particularly those designed for small targets, fall short in addressing the challenge that is posed by special regional-level multiscale recognition task. To this end, an end-to-end precise and swift road surface snow detection architecture, termed the Resource-Saving Snow Detect Model (RSSD) that includes a multidimensional directional attention mechanism, is proposed. In this model, we designed three dedicated modules, namely Multi-dimensional Bidirectional Attention Module (MDBA), Split-EMA-Convolution (SEC) and Equal Split Convolution (ESC), to address the essential feature extraction and fusion tasks in snow detection. MDBA is able to promote lateral interaction and comprehensive feature fusion across scales, while SEC can not only enhance feature extraction for regional awareness but also reduces computational load, making it efficient under minimal computational power consumption. ESC preserves feature height fusion while significantly reducing computational costs, thereby enhancing the real-time detection capability of the model. In experimental evaluations conducted with data collected by in-vehicle cameras from various roads in the United States and Canada, the results demonstrate higher detection accuracy and speed compared to the latest Transformer-based real-time object detection methods and other exiting methods in the literature. Furthermore, we validated the model's performance and data sensitivity through semi-supervised learning with 50,000 unlabeled images. This research holds significant implications for winter road traffic and provides valuable insights for similar computer vision tasks.

关键词： computer vision Convolution Computational modeling Snow Roads Object detection Feature extraction Transformers Real-time systems Computational efficiency

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PROCEEDINGS - CVPR '85, ieee computer SOCIETY conference ON computer vision AND pattern recognition.

PROCEEDINGS - CVPR '85, IEEE COMPUTER SOCIETY CONFERENCE ON ...

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Proceedings - CVPR '85, ieee computer Society conference on computer vision and pattern recognition.

作者： Anon

ISBN: (纸本)0818606339

The following topics are dealt with: edge and boundary analysis;vision systems;motion;shape and 2-D description;stereo and 3-D description;pattern recognition;3-D models;architectures;vision models and texture;image segmentation;applications and parallel algorithms;3-D analysis;contour analysis;character recognition;3-D descriptions from multiple views;and parallel architectures for image processing. 123 papers were presented, of which 121 are published in full in the present proceedings.

关键词： pattern recognition

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TRNeRF: Restoring Blurry, Rolling Shutter, and Noisy Thermal Images with Neural Radiance Fields

TRNeRF: Restoring Blurry, Rolling Shutter, and Noisy Thermal...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Spencer Carmichael Manohar Bhat Mani Ramanagopal Austin Buchan Ram Vasudevan Katherine A. Skinner University of Michigan Ann Arbor MI USA Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Thermal cameras offer unique detection capabilities in building inspections, search and rescue operations, and autonomous vehicle perception. Of the different types of thermal cameras, uncooled microbolometers are often chosen due to their relative affordability, small size, and low power consumption. However, microbolometers suffer from motion blur, rolling shutter distortions, and fixed pattern noise, which limit the conditions of their use. Nearly all prior methods for microbolometer image restoration account for only one of these degradations, and current techniques addressing microbolometer blur and rolling shutter are limited. This paper presents TRNeRF, a thermal image restoration method that jointly addresses all three degradations by incorporating the microbolometer image formation model with Neural Radiance Fields (NeRFs). To evaluate TRNeRF, this paper introduces a new real-world dataset that is uniquely designed to support two novel quantitative evaluation strategies for thermal image restoration. Experiments demonstrate that TRNeRF is able to recover sharp, global shutter, and clear thermal images, even under extremely aggressive camera motion that causes existing methods to fail. The code and dataset are available at: https://***/trnerf.

关键词： Degradation Power demand Noise Neural radiance field Cameras Distortion Rendering (computer graphics) Thermal noise Image restoration Thermal degradation

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