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PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery

PromptCAL: Contrastive Affinity Learning via Auxiliary Promp...

引用

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

作者： Zhang, Sheng Khan, Salman Shen, Zhiqiang Naseer, Muzammal Chen, Guangyi Khan, Fahad Shahbaz Mohamed bin Zayed Univ AI Abu Dhabi U Arab Emirates Hong Kong Univ Sci & Technol Hong Kong Peoples R China Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)9798350301298

Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled from novel semantic classes due to their closed-set assumption. In this work, we target a pragmatic but under-explored Generalized Novel Category Discovery (GNCD) setting. The GNCD setting aims to categorize unlabeled training data coming from known and novel classes by leveraging the information of partially labeled known classes. We propose a two-stage Contrastive Affinity Learning method with auxiliary visual Prompts, dubbed PromptCAL, to address this challenging problem. Our approach discovers reliable pairwise sample affinities to learn better semantic clustering of both known and novel classes for the class token and visual prompts. First, we propose a discriminative prompt regularization loss to reinforce semantic discriminativeness of prompt-adapted pre-trained vision transformer for refined affinity relationships. Besides, we propose contrastive affinity learning to calibrate semantic representations based on our iterative semi-supervised affinity graph generation method for semantically-enhanced supervision. Extensive experimental evaluation demonstrates that our PromptCAL method is more effective in discovering novel classes even with limited annotations and surpasses the current state-of-the-art on generic and fine-grained benchmarks (e.g., with nearly 11% gain on CUB-200, and 9% on ImageNet-100) on overall accuracy. Our code is available at https: // ***/ sheng- eatamath / PromptCAL.

关键词： detection recognition: Categorization retrieval

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OReX: Object Reconstruction from Planar Cross-sections Using Neural Fields

OReX: Object Reconstruction from Planar Cross-sections Using...

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

作者： Sawdayee, Haim Vaxman, Amir Bermano, Amit H. Tel Aviv Univ Blavatnik Sch Comp Sci Tel Aviv Israel Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland

ISBN: (纸本)9798350301298

Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the entire volume. Previous works addressing this sparse and ill-posed problem either produce low quality results, or rely on additional priors such as target topology, appearance information, or input normal directions. In this paper, we present OReX, a method for 3D shape reconstruction from slices alone, featuring a Neural Field as the interpolation prior. A modest neural network is trained on the input planes to return an inside/outside estimate for a given 3D coordinate, yielding a powerful prior that induces smoothness and self-similarities. The main challenge for this approach is high-frequency details, as the neural prior is overly smoothing. To alleviate this, we offer an iterative estimation architecture and a hierarchical input sampling scheme that encourage coarse-to-fine training, allowing the training process to focus on high frequencies at later stages. In addition, we identify and analyze a ripple-like effect stemming from the mesh extraction step. We mitigate it by regularizing the spatial gradients of the indicator function around input in/out boundaries during network training, tackling the problem at the root. Through extensive qualitative and quantitative experimentation, we demonstrate our method is robust, accurate, and scales well with the size of the input. We report state-of-the-art results compared to previous approaches and recent potential solutions, and demonstrate the benefit of our individual contributions through analysis and ablation studies.

关键词： Physics-based vision shape-from-X

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Balanced Product of Calibrated Experts for Long-Tailed recognition

Balanced Product of Calibrated Experts for Long-Tailed Recog...

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

作者： Aimar, Emanuel Sanchez Jonnarth, Arvi Felsberg, Michael Kuhlmann, Marco Linkoping Univ Dept Elect Engn Linkoping Sweden Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Husqvarna Grp Huskvarna Sweden Univ KwaZulu Natal Durban South Africa

ISBN: (纸本)9798350301298

Many real-world recognition problems are characterized by long-tailed label distributions. These distributions make representation learning highly challenging due to limited generalization over the tail classes. If the test distribution differs from the training distribution, e.g. uniform versus long-tailed, the problem of the distribution shift needs to be addressed. A recent line of work proposes learning multiple diverse experts to tackle this issue. Ensemble diversity is encouraged by various techniques, e.g. by specializing different experts in the head and the tail classes. In this work, we take an analytical approach and extend the notion of logit adjustment to ensembles to form a Balanced Product of Experts (BalPoE). BalPoE combines a family of experts with different test-time target distributions, generalizing several previous approaches. We show how to properly define these distributions and combine the experts in order to achieve unbiased predictions, by proving that the ensemble is Fisher-consistent for minimizing the balanced error. Our theoretical analysis shows that our balanced ensemble requires calibrated experts, which we achieve in practice using mixup. We conduct extensive experiments and our method obtains new state-of-the-art results on three long-tailed datasets: CIFAR-100-LT, ImageNet-LT, and iNaturalist-2018. Our code is available at https://***/emasa/BalPoE-CalibratedLT.

关键词： continual low-shot meta or long-tail learning Transfer

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Bias Mimicking: A Simple Sampling Approach for Bias Mitigation

Bias Mimicking: A Simple Sampling Approach for Bias Mitigati...

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

作者： Qraitem, Maan Saenko, Kate Plummer, Bryan A. Boston Univ Boston MA 02215 USA MIT IBM Watson Lab Cambridge MA USA

ISBN: (纸本)9798350301298

Prior work has shown that Visual recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address this problem require significant architectural changes or additional loss functions requiring more hyper-parameter tuning. Alternatively, data sampling baselines from the class imbalance literature (e.g. Undersampling, Upweighting), which can often be implemented in a single line of code and often have no hyper-parameters, offer a cheaper and more efficient solution. However, these methods suffer from significant shortcomings. For example, Undersampling drops a significant part of the input distribution per epoch while Oversampling repeats samples, causing overfitting. To address these shortcomings, we introduce a new class-conditioned sampling method: Bias Mimicking. The method is based on the observation that if a class c bias distribution, i.e. PD(B| Y = c) is mimicked across every c ' not equal c, then Y and B are statistically independent. Using this notion, BM, through a novel training procedure, ensures that the model is exposed to the entire distribution per epoch without repeating samples. Consequently, Bias Mimicking improves underrepresented groups' accuracy of sampling methods by 3% over four benchmarks while maintaining and sometimes improving performance over nonsampling methods. Code: https: //***/mqraitem/Bias-Mimicking

关键词： accountability accountability ethics in vision fairness privacy Transparency

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Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting

Towards High-Quality and Efficient Video Super-Resolution vi...

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

作者： Li, Gen Ji, Jie Qin, Minghai Niu, Wei Ren, Bin Afghah, Fatemeh Guo, Linke Ma, Xiaolong Clemson Univ Clemson SC 29631 USA William & Mary Williamsburg VA USA

ISBN: (纸本)9798350301298

As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolution model, the server encodes videos before transmitting them to the clients, thus achieving better video quality and transmission efficiency. However, a large number of chunks are expected to ensure good overfitting quality, which substantially increases the storage and consumes more bandwidth resources for data transmission. On the other hand, decreasing the number of chunks through training optimization techniques usually requires high model capacity, which significantly slows down execution speed. To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum. Additionally, we advance our method into a single overfitting model by a data-aware joint training technique, which further reduces the storage requirement with negligible quality drop. We deploy our models on an off-the-shelf mobile phone, and experimental results show that our method achieves real-time video super-resolution with high video quality. Compared with the state-of-the-art, our method achieves 28 fps streaming speed with 41.6 PSNR, which is 14x faster and 2.29 dB better in the live video resolution upscaling tasks. Code available in https://***/coulsonlee/***.

关键词： Efficient and scalable vision

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The Casual Conversations v2 Dataset A diverse, large benchmark for measuring fairness and robustness in audio/vision/speech models

The Casual Conversations v2 Dataset A diverse, large benchma...

引用

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

作者： Porgali, Bilal Albiero, Vitor Ryda, Jordan Ferrer, Cristian Canton Hazirbas, Caner Meta AI Menlo Pk CA 94025 USA

ISBN: (纸本)9798350302493

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

关键词： Large dataset

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Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection

Object-Aware Distillation Pyramid for Open-Vocabulary Object...

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

作者： Wang, Luting Liu, Yi Du, Penghui Ding, Zihan Liao, Yue Qi, Qiaosong Chen, Biaolong Liu, Si Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China Beihang Univ Hangzhou Innovat Inst Beijing Peoples R China

ISBN: (纸本)9798350301298

Open-vocabulary object detection aims to provide object detectors trained on a fixed set of object categories with the generalizability to detect objects described by arbitrary text queries. Previous methods adopt knowledge distillation to extract knowledge from Pretrained vision-and-Language Models (PVLMs) and transfer it to detectors. However, due to the non-adaptive proposal cropping and single-level feature mimicking processes, they suffer from information destruction during knowledge extraction and inefficient knowledge transfer. To remedy these limitations, we propose an Object-Aware Distillation Pyramid (OADP) framework, including an Object-Aware Knowledge Extraction (OAKE) module and a Distillation Pyramid (DP) mechanism. When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects. The latter introduces global and block distillation for more comprehensive knowledge transfer to compensate for the missing relation information in object distillation. Extensive experiments show that our method achieves significant improvement compared to current methods. Especially on the MS-COCO dataset, our OADP framework reaches 35.6 mAP(50)(N), surpassing the current state-of-the-art method by 3.3 mAP(50)(N). Code is released at https://***/LutingWang/OADP.

关键词： detection recognition: Categorization retrieval

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Visual Exemplar Driven Task-Prompting for Unified Perception in Autonomous Driving

Visual Exemplar Driven Task-Prompting for Unified Perception...

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

作者： Liang, Xiwen Niu, Minzhe Han, Jianhua Xu, Hang Xu, Chunjing Liang, Xiaodan Sun Yat Sen Univ Shenzhen Campus Guangzhou Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9798350301298

Multi-task learning has emerged as a powerful paradigm to solve a range of tasks simultaneously with good efficiency in both computation resources and inference time. However, these algorithms are designed for different tasks mostly not within the scope of autonomous driving, thus making it hard to compare multi-task methods in autonomous driving. Aiming to enable the comprehensive evaluation of present multi-task learning methods in autonomous driving, we extensively investigate the performance of popular multi-task methods on the large-scale driving dataset, which covers four common perception tasks, i.e., object detection, semantic segmentation, drivable area segmentation, and lane detection. We provide an in-depth analysis of current multi-task learning methods under different common settings and find out that the existing methods make progress but there is still a large performance gap compared with single-task baselines. To alleviate this dilemma in autonomous driving, we present an effective multi-task framework, VE-Prompt, which introduces visual exemplars via task-specific prompting to guide the model toward learning high-quality task-specific representations. Specifically, we generate visual exemplars based on bounding boxes and color-based markers, which provide accurate visual appearances of target categories and further mitigate the performance gap. Furthermore, we bridge transformer-based encoders and convolutional layers for efficient and accurate unified perception in autonomous driving. Comprehensive experimental results on the diverse self-driving dataset BDD100K show that the VE-Prompt improves the multi-task baseline and further surpasses single-task models.

关键词： vision applications and systems

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SigmML: Metric meta-learning for Writer Independent Offline Signature Verification in the Space of SPD Matrices

SigmML: Metric meta-learning for Writer Independent Offline ...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Giazitzis, Alexios Zois, Elias N. UniWA Telsip Lab Petrou Ralli & Thivon Ave Egaleo 12241 Greece

ISBN: (纸本)9798350318920;9798350318937

The handwritten signature has been identified as one of the most popular biometric means of human consent and/or presence for transactions held by any number of physical or legal entities. Automated signature verification (ASV), merge popular scientific branches such as computer vision, pattern recognition and/or data-driven machine learning algorithms. Up to now, several metric learning approaches for designing a writer-independent signature verifier, have been developed within a Euclidean framework by means of having their operations closed with respect to real vector spaces. In this work, we propose, for the first time in the ASV literature, the use of a meta-learning framework in the space of the Symmetric Positive Definite (SPD) manifold as a means to learn a pairwise similarity metric for writer-independent ASV. To begin, pairs of handwritten signatures are converted into a multidimensional distance vector with elements corresponding SPD distances between spatial segments of corresponding covariance pairs. We propose a novel meta-learning approach which explores the structure of the input gradients of the SPD manifold by means of a recurrent model, constrained by the geometry of the SPD manifold. The experimental protocols utilize two popular signature datasets of Western and Asian origin in two blind-intra and blind-inter (or cross-lingual) transfer learning approach. It also provide evidence of the discriminating nature of the proposed framework at least when summarized against other State-of-the-Art models, typically realized under a framework of Euclidean, or vector space, nature.

关键词： Algorithms Algorithms Algorithms and algorithms Biometrics body pose face formulations gesture Image recognition and understanding Machine learning architectures

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Holistic Representation Learning for Multitask Trajectory Anomaly Detection

Holistic Representation Learning for Multitask Trajectory An...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Stergiou, Alexandros De Weerdt, Brent Deligiannis, Nikos Vrije Univ Brussel Brussels Belgium IMEC Leuven Belgium

ISBN: (纸本)9798350318920;9798350318937

Video anomaly detection deals with the recognition of abnormal events in videos. Apart from the visual signal, video anomaly detection has also been addressed with the use of skeleton sequences. We propose a holistic representation of skeleton trajectories to learn expected motions across segments at different times. Our approach uses multitask learning to reconstruct any continuous unobserved temporal segment of the trajectory allowing the extrapolation of past or future segments and the interpolation of inbetween segments. We use an end-to-end attention-based encoder-decoder. We encode temporally occluded trajectories, jointly learn latent representations of the occluded segments, and reconstruct trajectories based on expected motions across different temporal segments. Extensive experiments on three trajectory-based video anomaly detection datasets show the advantages and effectiveness of our approach with state-of-the-art results on anomaly detection in skeleton trajectories(dagger).

关键词： Algorithms Video recognition and understanding

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