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MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual recognition

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual ...

引用

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

作者： Li, Shuang Gong, Kaixiong Liu, Chi Harold Wang, Yulin Qiao, Feng Cheng, Xinjing Beijing Inst Technol Beijing Peoples R China Tsinghua Univ Beijing Peoples R China Inceptio Tech Fremont CA USA

ISBN: (纸本)9781665445092

Real-world training data usually exhibits long-tailed distribution, where several majority classes have a significantly larger number of samples than the remaining minority classes. This imbalance degrades the performance of typical supervised learning algorithms designed for balanced training sets. In this paper, we address this issue by augmenting minority classes with a recently proposed implicit semantic data augmentation (ISDA) algorithm [37] which produces diversified augmented samples by translating deep features along many semantically meaningful directions. Importantly, given that ISDA estimates the class-conditional statistics to obtain semantic directions, we find it ineffective to do this on minority classes due to the insufficient training data. To this end, we propose a novel approach to learn transformed semantic directions with metalearning automatically. In specific, the augmentation strategy during training is dynamically optimized, aiming to minimize the loss on a small balanced validation set, which is approximated via a meta update step. Extensive empirical results on CIFAR-LT-10/100, ImageNet-LT, and iNaturalist 2017/2018 validate the effectiveness of our method.

关键词： Training Visualization computer vision Semantics Supervised learning Training data Benchmark testing

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4D Hyperspectral Photoacoustic Data Restoration with Reliability Analysis

4D Hyperspectral Photoacoustic Data Restoration with Reliabi...

引用

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

作者： Liao, Weihang Subpa-Asa, Art Zheng, Yinqiang Sato, Imari Tokyo Inst Technol Tokyo Japan Natl Inst Informat Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781665445092

Hyperspectral photoacoustic (HSPA) spectroscopy is an emerging bi-modal imaging technology that is able to show the wavelength-dependent absorption distribution of the interior of a 3D volume. However, HSPA devices have to scan an object exhaustively in the spatial and spectral domains;and the acquired data tend to suffer from complex noise. This time-consuming scanning process and noise severely affects the usability of HSPA. It is therefore critical to examine the feasibility of 4D HSPA data restoration from an incomplete and noisy observation. In this work, we present a data reliability analysis for the depth and spectral domain. On the basis of this analysis, we explore the inherent data correlations and develop a restoration algorithm to recover 4D HSPA cubes. Experiments on real data verify that the proposed method achieves satisfactory restoration results.

关键词： Legged locomotion Spectroscopy Correlation Three-dimensional displays pattern recognition Reliability Noise measurement

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ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG-based Video Analysis System

ViTA: An Efficient Video-to-Text Algorithm using VLM for RAG...

引用

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

作者： Md Adnan Arefeen Biplob Debnath Md Yusuf Sarwar Uddin Srimat Chakradhar NEC Laboratories America University of Missouri-Kansas City

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Retrieval-augmented generation (RAG) is used in natural language processing (NLP) to provide query-relevant information in enterprise documents to large language models (LLMs). Such enterprise context enables the LLMs to generate more informed and accurate responses. When enterprise data is primarily videos, AI models like vision language models (VLMs) are necessary to convert information in videos into text. While essential, this conversion is a bottleneck, especially for large corpus of videos. It delays the timely use of enterprise videos to generate useful *** propose ViTA, a novel method that leverages two unique characteristics of VLMs to expedite the conversion process. As VLMs output more text tokens, they incur higher latency. In addition, large (heavyweight) VLMs can extract intricate details from images and videos, but they incur much higher latency per output token when compared to smaller (lightweight) VLMs that may miss details. To expedite conversion, ViTA first employs a lightweight VLM to quickly understand the gist or overview of an image or a video clip, and directs a heavyweight VLM (through prompt engineering) to extract additional details by using only a few (preset number of) output tokens. Our experimental results show that ViTA expedites the conversion time by as much as 43%, without compromising the accuracy of responses when compared to a baseline system that only uses a heavyweight VLM.

关键词： computer vision Accuracy Large language models conferences Natural language processing Data models pattern recognition

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Learning Deep Latent Variable Models by Short-Run MCMC Inference with Optimal Transport Correction

Learning Deep Latent Variable Models by Short-Run MCMC Infer...

引用

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

作者： An, Dongsheng Xie, Jianwen Li, Ping Baidu Res Cognit Comp Lab 10900 NE 8th St Bellevue WA 98004 USA

ISBN: (纸本)9781665445092

Learning latent variable models with deep top-down architectures typically requires inferring the latent variables for each training example based on the posterior distribution of these latent variables. The inference step typically relies on either time-consuming long-run Markov chain Monte Carlo (MCMC) sampling or a separate inference model for variational learning. In this paper, we propose to use a short-run MCMC, such as a short-run Langevin dynamics, as an approximate flow-based inference engine. The bias existing in the output distribution of the non-convergent short-run Langevin dynamics is corrected by the optimal transport (OT), which aims at transforming the biased distribution produced by the finite-step MCMC to the prior distribution with a minimum transport cost. Our experiments not only verify the effectiveness of the OT correction for the short-run MCMC, but also demonstrate that the latent variable model trained by the proposed strategy performs better than the variational auto-encoder (VAE) in terms of image reconstruction/generation and anomaly detection.

关键词： Training Monte Carlo methods Costs Image synthesis Markov processes pattern recognition Task analysis

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GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation

GDR-Net: Geometry-Guided Direct Regression Network for Monoc...

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

作者： Wang, Gu Manhardt, Fabian Tombari, Federico Ji, Xiangyang Tsinghua Univ BNRist Beijing Peoples R China Tech Univ Munich Munich Germany Google Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

6D pose estimation from a single RGB image is a fundamental task in computer vision. The current top-performing deep learning-based methods rely on an indirect strategy, i.e., first establishing 2D-3D correspondences between the coordinates in the image plane and object coordinate system, and then applying a variant of the PnP/RANSAC algorithm. However, this two-stage pipeline is not end-toend trainable, thus is hard to be employed for many tasks requiring differentiable poses. On the other hand, methods based on direct regression are currently inferior to geometry-based methods. In this work, we perform an indepth investigation on both direct and indirect methods, and propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner from dense correspondence-based intermediate geometric representations. Extensive experiments show that our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets. Code is available at https://***/GDR-Net.

关键词： Learning systems Convolutional codes computer vision Pose estimation Pipelines Real-time systems pattern recognition

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Semi-Supervised Action recognition with Temporal Contrastive Learning

Semi-Supervised Action Recognition with Temporal Contrastive...

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

作者： Singh, Ankit Chakraborty, Omprakash Varshney, Ashutosh Panda, Rameswar Feris, Rogerio Saenko, Kate Das, Abir IIT Madras Chennai Tamil Nadu India IIT Kharagpur Kharagpur W Bengal India MIT IBM Watson AI Lab Cambridge MA USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781665445092

Learning to recognize actions from only a handful of labeled videos is a challenging problem due to the scarcity of tediously collected activity labels. We approach this problem by learning a two-pathway temporal contrastive model using unlabeled videos at two different speeds leveraging the fact that changing video speed does not change an action. Specifically, we propose to maximize the similarity between encoded representations of the same video at two different speeds as well as minimize the similarity between different videos played at different speeds. This way we use the rich supervisory information in terms of 'time' that is present in otherwise unsupervised pool of videos. With this simple yet effective strategy of manipulating video playback rates, we considerably outperform video extensions of sophisticated state-of-the-art semi-supervised image recognition methods across multiple diverse benchmark datasets and network architectures. Interestingly, our proposed approach benefits from out-of-domain unlabeled videos showing generalization and robustness. We also perform rigorous ablations and analysis to validate our approach.

关键词： computer vision Image recognition Semantics Network architecture Benchmark testing Robustness pattern recognition

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pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis

pi-GAN: Periodic Implicit Generative Adversarial Networks fo...

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

作者： Chan, Eric R. Monteiro, Marco Kellnhofer, Petr Wu, Jiajun Wetzstein, Gordon Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9781665445092

We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering. Existing approaches however fall short in two ways: first, they may lack an underlying 3D representation or rely on view-inconsistent rendering, hence synthesizing images that are not multi-view consistent;second, they often depend upon representation network architectures that are not expressive enough, and their results thus lack in image quality. We propose a novel generative model, named Periodic Implicit Generative Adversarial Networks (pi-GAN or pi-GAN), for high-quality 3D-aware image synthesis. pi-GAN leverages neural representations with periodic activation functions and volumetric rendering to represent scenes as view-consistent radiance fields. The proposed approach obtains state-of-the-art results for 3D-aware image synthesis with multiple real and synthetic datasets.

关键词： Image quality Visualization computer vision Three-dimensional displays Image synthesis Network architecture Rendering (computer graphics)

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Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Towards Good Practices for Efficiently Annotating Large-Scal...

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

作者： Liao, Yuan-Hong Kar, Amlan Fidler, Sanja Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada NVIDIA Santa Clara CA USA

ISBN: (纸本)9781665445092

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human labelers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively.(1)

关键词： computer vision Analytical models Annotations Manuals Semisupervised learning Probabilistic logic pattern recognition

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Continuous Face Aging via Self-estimated Residual Age Embedding

Continuous Face Aging via Self-estimated Residual Age Embedd...

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

作者： Li, Zeqi Jiang, Ruowei Aarabi, Parham ModiFace Toronto ON Canada

ISBN: (纸本)9781665445092

Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage groupbased training strategies, which lacks the ability to provide fine-controlled continuous aging synthesis in nature. In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression. The personalized target age embedding is synthesized by incorporating both personalized residual age embedding of the current age and exemplar-face aging basis of the target age, where all preceding aging bases are derived from the learned weights of the linear age estimator. This formulation brings the unified perspective of estimating the age and generating personalized aged face, where self-estimated age embeddings can be learned for every single age. The qualitative and quantitative evaluations on different datasets further demonstrate the significant improvement in the continuous face aging aspect over the state-of-the-art.

关键词： Training computer vision Face recognition Estimation Aging Generative adversarial networks Generators

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Towards Open World Object Detection

Towards Open World Object Detection

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

作者： Joseph, K. J. Khan, Salman Khan, Fahad Shahbaz Balasubramanian, Vineeth N. Indian Inst Technol Hyderabad Hyderabad India Mohamed Bin Zayed Univ AI Abu Dhabi U Arab Emirates Australian Natl Univ Canberra ACT Australia Linkoping Univ Linkoping Sweden

ISBN: (纸本)9781665445092

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when the corresponding knowledge is eventually available. This motivates us to propose a novel computer vision problem called: 'Open World Object Detection', where a model is tasked to: 1) identify objects that have not been introduced to it as 'unknown', without explicit supervision to do so, and 2) incrementally learn these identified unknown categories without forgetting previously learned classes, when the corresponding labels are progressively received. We formulate the problem, introduce a strong evaluation protocol and provide a novel solution, which we call ORE: Open World Object Detector, based on contrastive clustering and energy based unknown identification. Our experimental evaluation and ablation studies analyse the efficacy of ORE in achieving Open World objectives. As an interesting by-product, we find that identifying and characterising unknown instances helps to reduce confusion in an incremental object detection setting, where we achieve state-ofthe-art performance, with no extra methodological effort. We hope that our work will attract further research into this newly identified, yet crucial research direction.'

关键词： computer vision Protocols Computational modeling Object detection Detectors Benchmark testing pattern recognition

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