pwc-archive/papers-with-abstracts huggingface dataset:
DiffuPose: Monocular 3D Human Pose Estimation via Denoising Diffusion Probabilistic Model
Authors: Jeongjun Choi, Dongseok Shim, H. Jin Kim
Date: 2022-12-06 00:00:00
Tasks: 3D Human Pose Estimation, Denoising, Keypoint Detection, Monocular 3D Human Pose Estimation, Pose Estimation

Thanks to the development of 2D keypoint detectors, monocular 3D human pose estimation (HPE) via 2D-to-3D uplifting approaches have achieved remarkable improvements. Still, monocular 3D HPE is a challenging problem due to the inherent depth ambiguities and occlusions. To handle this problem, many previous works exploit temporal information to mitigate such difficulties. However, there are many real-world applications where frame sequences are not accessible. This paper focuses on reconstructing a 3D pose from a single 2D keypoint detection. Rather than exploiting temporal information, we alleviate the depth ambiguity by generating multiple 3D pose candidates which can be mapped to an identical 2D keypoint. We build a novel diffusion-based framework to effectively sample diverse 3D poses from an off-the-shelf 2D detector. By considering the correlation between human joints by replacing the conventional denoising U-Net with graph convolutional network, our approach accomplishes further performance improvements. We evaluate our method on the widely adopted Human3.6M and HumanEva-I datasets. Comprehensive experiments are conducted to prove the efficacy of the proposed method, and they confirm that our model outperforms state-of-the-art multi-hypothesis 3D HPE methods.
Hybrid Model using Feature Extraction and Non-linear SVM for Brain Tumor Classification
Authors: Lalita Mishra, Shekhar Verma, Shirshu Varma
Date: 2022-12-06 00:00:00
Tasks: Binary Classification, Brain Tumor Classification

It is essential to classify brain tumors from magnetic resonance imaging (MRI) accurately for better and timely treatment of the patients. In this paper, we propose a hybrid model, using VGG along with Nonlinear-SVM (Soft and Hard) to classify the brain tumors: glioma and pituitary and tumorous and non-tumorous. The VGG-SVM model is trained for two different datasets of two classes; thus, we perform binary classification. The VGG models are trained via the PyTorch python library to obtain the highest testing accuracy of tumor classification. The method is threefold, in the first step, we normalize and resize the images, and the second step consists of feature extraction through variants of the VGG model. The third step classified brain tumors using non-linear SVM (soft and hard). We have obtained 98.18% accuracy for the first dataset and 99.78% for the second dataset using VGG19. The classification accuracies for non-linear SVM are 95.50% and 97.98% with linear and rbf kernel and 97.95% for soft SVM with RBF kernel with D1, and 96.75% and 98.60% with linear and RBF kernel and 98.38% for soft SVM with RBF kernel with D2. Results indicate that the hybrid VGG-SVM model, especially VGG 19 with SVM, is able to outperform existing techniques and achieve high accuracy.
Event-based Monocular Dense Depth Estimation with Recurrent Transformers
Authors: Xu Liu, Jianing Li, Xiaopeng Fan, Yonghong Tian
Date: 2022-12-06 00:00:00
Tasks: Decoder, Depth Estimation, Event-based vision, GPU, Monocular Depth Estimation

Event cameras, offering high temporal resolutions and high dynamic ranges, have brought a new perspective to address common challenges (e.g., motion blur and low light) in monocular depth estimation. However, how to effectively exploit the sparse spatial information and rich temporal cues from asynchronous events remains a challenging endeavor. To this end, we propose a novel event-based monocular depth estimator with recurrent transformers, namely EReFormer, which is the first pure transformer with a recursive mechanism to process continuous event streams. Technically, for spatial modeling, a novel transformer-based encoder-decoder with a spatial transformer fusion module is presented, having better global context information modeling capabilities than CNN-based methods. For temporal modeling, we design a gate recurrent vision transformer unit that introduces a recursive mechanism into transformers, improving temporal modeling capabilities while alleviating the expensive GPU memory cost. The experimental results show that our EReFormer outperforms state-of-the-art methods by a margin on both synthetic and real-world datasets. We hope that our work will attract further research to develop stunning transformers in the event-based vision community. Our open-source code can be found in the supplemental material.
Adaptive Testing of Computer Vision Models
Authors: Irena Gao, Gabriel Ilharco, Scott Lundberg, Marco Tulio Ribeiro
Date: 2022-12-06 00:00:00
Proceeding: ICCV 2023 1
Tasks: Image Captioning, object-detection, Object Detection, Retrieval

Vision models often fail systematically on groups of data that share common semantic characteristics (e.g., rare objects or unusual scenes), but identifying these failure modes is a challenge. We introduce AdaVision, an interactive process for testing vision models which helps users identify and fix coherent failure modes. Given a natural language description of a coherent group, AdaVision retrieves relevant images from LAION-5B with CLIP. The user then labels a small amount of data for model correctness, which is used in successive retrieval rounds to hill-climb towards high-error regions, refining the group definition. Once a group is saturated, AdaVision uses GPT-3 to suggest new group descriptions for the user to explore. We demonstrate the usefulness and generality of AdaVision in user studies, where users find major bugs in state-of-the-art classification, object detection, and image captioning models. These user-discovered groups have failure rates 2-3x higher than those surfaced by automatic error clustering methods. Finally, finetuning on examples found with AdaVision fixes the discovered bugs when evaluated on unseen examples, without degrading in-distribution accuracy, and while also improving performance on out-of-distribution datasets.
CSQ: Growing Mixed-Precision Quantization Scheme with Bi-level Continuous Sparsification
Authors: Lirui Xiao, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang
Date: 2022-12-06 00:00:00
Tasks: Quantization

Mixed-precision quantization has been widely applied on deep neural networks (DNNs) as it leads to significantly better efficiency-accuracy tradeoffs compared to uniform quantization. Meanwhile, determining the exact precision of each layer remains challenging. Previous attempts on bit-level regularization and pruning-based dynamic precision adjustment during training suffer from noisy gradients and unstable convergence. In this work, we propose Continuous Sparsification Quantization (CSQ), a bit-level training method to search for mixed-precision quantization schemes with improved stability. CSQ stabilizes the bit-level mixed-precision training process with a bi-level gradual continuous sparsification on both the bit values of the quantized weights and the bit selection in determining the quantization precision of each layer. The continuous sparsification scheme enables fully-differentiable training without gradient approximation while achieving an exact quantized model in the end.A budget-aware regularization of total model size enables the dynamic growth and pruning of each layer's precision towards a mixed-precision quantization scheme of the desired size. Extensive experiments show CSQ achieves better efficiency-accuracy tradeoff than previous methods on multiple models and datasets.
Ref-NPR: Reference-Based Non-Photorealistic Radiance Fields for Controllable Scene Stylization
Authors: Yuechen Zhang, Zexin He, Jinbo Xing, Xufeng Yao, Jiaya Jia
Date: 2022-12-06 00:00:00
Proceeding: CVPR 2023 1
Tasks: Semantic correspondence

Current 3D scene stylization methods transfer textures and colors as styles using arbitrary style references, lacking meaningful semantic correspondences. We introduce Reference-Based Non-Photorealistic Radiance Fields (Ref-NPR) to address this limitation. This controllable method stylizes a 3D scene using radiance fields with a single stylized 2D view as a reference. We propose a ray registration process based on the stylized reference view to obtain pseudo-ray supervision in novel views. Then we exploit semantic correspondences in content images to fill occluded regions with perceptually similar styles, resulting in non-photorealistic and continuous novel view sequences. Our experimental results demonstrate that Ref-NPR outperforms existing scene and video stylization methods regarding visual quality and semantic correspondence. The code and data are publicly available on the project page at https://ref-npr.github.io.
Pixel2ISDF: Implicit Signed Distance Fields based Human Body Model from Multi-view and Multi-pose Images
Authors: Jianchuan Chen, Wentao Yi, Tiantian Wang, Xing Li, Liqian Ma, Yangyu Fan, Huchuan Lu
Date: 2022-12-06 00:00:00

In this report, we focus on reconstructing clothed humans in the canonical space given multiple views and poses of a human as the input. To achieve this, we utilize the geometric prior of the SMPLX model in the canonical space to learn the implicit representation for geometry reconstruction. Based on the observation that the topology between the posed mesh and the mesh in the canonical space are consistent, we propose to learn latent codes on the posed mesh by leveraging multiple input images and then assign the latent codes to the mesh in the canonical space. Specifically, we first leverage normal and geometry networks to extract the feature vector for each vertex on the SMPLX mesh. Normal maps are adopted for better generalization to unseen images compared to 2D images. Then, features for each vertex on the posed mesh from multiple images are integrated by MLPs. The integrated features acting as the latent code are anchored to the SMPLX mesh in the canonical space. Finally, latent code for each 3D point is extracted and utilized to calculate the SDF. Our work for reconstructing the human shape on canonical pose achieves 3rd performance on WCPA MVP-Human Body Challenge.
A Trustworthy Framework for Medical Image Analysis with Deep Learning
Authors: Kai Ma, Siyuan He, Pengcheng Xi, Ashkan Ebadi, Stéphane Tremblay, Alexander Wong
Date: 2022-12-06 00:00:00
Tasks: Deep Learning, Medical Image Analysis

Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the framework are both trustworthy and high-performing. It is anticipated that the framework will support researchers and clinicians in advancing the use of deep learning for dealing with public health crises including COVID-19.
Semi-supervised Deep Large-baseline Homography Estimation with Progressive Equivalence Constraint
Authors: Hai Jiang, Haipeng Li, Yuhang Lu, Songchen Han, Shuaicheng Liu
Date: 2022-12-06 00:00:00
Tasks: Homography Estimation

Homography estimation is erroneous in the case of large-baseline due to the low image overlay and limited receptive field. To address it, we propose a progressive estimation strategy by converting large-baseline homography into multiple intermediate ones, cumulatively multiplying these intermediate items can reconstruct the initial homography. Meanwhile, a semi-supervised homography identity loss, which consists of two components: a supervised objective and an unsupervised objective, is introduced. The first supervised loss is acting to optimize intermediate homographies, while the second unsupervised one helps to estimate a large-baseline homography without photometric losses. To validate our method, we propose a large-scale dataset that covers regular and challenging scenes. Experiments show that our method achieves state-of-the-art performance in large-baseline scenes while keeping competitive performance in small-baseline scenes. Code and dataset are available at https://github.com/megvii-research/LBHomo.
Learning Neural Parametric Head Models
Authors: Simon Giebenhain, Tobias Kirschstein, Markos Georgopoulos, Martin Rünz, Lourdes Agapito, Matthias Nießner
Date: 2022-12-06 00:00:00
Proceeding: CVPR 2023 1

We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 5200 head scans from 255 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality.
Attention-Enhanced Cross-modal Localization Between 360 Images and Point Clouds
Authors: Zhipeng Zhao, Huai Yu, Chenwei Lyv, Wen Yang, Sebastian Scherer
Date: 2022-12-06 00:00:00
Tasks: Autonomous Driving, Camera Localization, Visual Localization

Visual localization plays an important role for intelligent robots and autonomous driving, especially when the accuracy of GNSS is unreliable. Recently, camera localization in LiDAR maps has attracted more and more attention for its low cost and potential robustness to illumination and weather changes. However, the commonly used pinhole camera has a narrow Field-of-View, thus leading to limited information compared with the omni-directional LiDAR data. To overcome this limitation, we focus on correlating the information of 360 equirectangular images to point clouds, proposing an end-to-end learnable network to conduct cross-modal visual localization by establishing similarity in high-dimensional feature space. Inspired by the attention mechanism, we optimize the network to capture the salient feature for comparing images and point clouds. We construct several sequences containing 360 equirectangular images and corresponding point clouds based on the KITTI-360 dataset and conduct extensive experiments. The results demonstrate the effectiveness of our approach.
Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty
Authors: Honggyu Choi, Zhixiang Chen, Xuepeng Shi, Tae-Kyun Kim
Date: 2022-12-06 00:00:00
Tasks: Classification, Contrastive Learning, Object, object-detection, Object Detection, Object Localization, Pseudo Label, Pseudo Label Filtering, regression, Representation Learning, Semi-Supervised Object Detection

Semi-supervised object detection (SSOD) aims to boost detection performance by leveraging extra unlabeled data. The teacher-student framework has been shown to be promising for SSOD, in which a teacher network generates pseudo-labels for unlabeled data to assist the training of a student network. Since the pseudo-labels are noisy, filtering the pseudo-labels is crucial to exploit the potential of such framework. Unlike existing suboptimal methods, we propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework. For the classification head, OCL (Object-wise Contrastive Learning) regularizes the object representation learning that utilizes unlabeled data to improve pseudo-label filtering by enhancing the discriminativeness of the classification score. This is designed to pull together objects in the same class and push away objects from different classes. For the regression head, we further propose RUPL (Regression-Uncertainty-guided Pseudo-Labeling) to learn the aleatoric uncertainty of object localization for label filtering. By jointly filtering the pseudo-labels for the classification and regression heads, the student network receives better guidance from the teacher network for object detection task. Experimental results on Pascal VOC and MS-COCO datasets demonstrate the superiority of our proposed method with competitive performance compared to existing methods.
Vision Transformer Computation and Resilience for Dynamic Inference
Authors: Kavya Sreedhar, Jason Clemons, Rangharajan Venkatesan, Stephen W. Keckler, Mark Horowitz
Date: 2022-12-06 00:00:00
Tasks: GPU, Semantic Segmentation

State-of-the-art deep learning models for computer vision tasks are based on the transformer architecture and often deployed in real-time applications. In this scenario, the resources available for every inference can vary, so it is useful to be able to dynamically adapt execution to trade accuracy for efficiency. To create dynamic models, we leverage the resilience of vision transformers to pruning and switch between different scaled versions of a model. Surprisingly, we find that most FLOPs are generated by convolutions, not attention. These relative FLOP counts are not a good predictor of GPU performance since GPUs have special optimizations for convolutions. Some models are fairly resilient and their model execution can be adapted without retraining, while all models achieve better accuracy with retraining alternative execution paths. These insights mean that we can leverage CNN accelerators and these alternative execution paths to enable efficient and dynamic vision transformer inference. Our analysis shows that leveraging this type of dynamic execution can lead to saving 28\% of energy with a 1.4\% accuracy drop for SegFormer (63 GFLOPs), with no additional training, and 53\% of energy for ResNet-50 (4 GFLOPs) with a 3.3\% accuracy drop by switching between pretrained Once-For-All models.
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances
Authors: Ajay Jaiswal, Tianlong Chen, Justin F. Rousseau, Yifan Peng, Ying Ding, Zhangyang Wang
Date: 2022-12-06 00:00:00
Tasks: image-classification, Image Classification, Network Pruning

Deep neural networks (DNNs) have rapidly become a \textit{de facto} choice for medical image understanding tasks. However, DNNs are notoriously fragile to the class imbalance in image classification. We further point out that such imbalance fragility can be amplified when it comes to more sophisticated tasks such as pathology localization, as imbalances in such problems can have highly complex and often implicit forms of presence. For example, different pathology can have different sizes or colors (w.r.t.the background), different underlying demographic distributions, and in general different difficulty levels to recognize, even in a meticulously curated balanced distribution of training data. In this paper, we propose to use pruning to automatically and adaptively identify \textit{hard-to-learn} (HTL) training samples, and improve pathology localization by attending them explicitly, during training in \textit{supervised, semi-supervised, and weakly-supervised} settings. Our main inspiration is drawn from the recent finding that deep classification models have difficult-to-memorize samples and those may be effectively exposed through network pruning \cite{hooker2019compressed} - and we extend such observation beyond classification for the first time. We also present an interesting demographic analysis which illustrates HTLs ability to capture complex demographic imbalances. Our extensive experiments on the Skin Lesion Localization task in multiple training settings by paying additional attention to HTLs show significant improvement of localization performance by $\sim$2-3\%.
Continual learning on deployment pipelines for Machine Learning Systems
Authors: Qiang Li, Chongyu Zhang
Date: 2022-12-05 00:00:00
Tasks: Anomaly Detection, Continual Learning

Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants. Deployment of such a system is becoming an extremely important topic. Our work starts with the least-automated deployment technologies of machine learning systems includes several iterations of updates, and ends with a comparison of automated deployment techniques. The objective is, on the one hand, to compare the advantages and disadvantages of various technologies in theory and practice, so as to facilitate later adopters to avoid making the generalized mistakes when implementing actual use cases, and thereby choose a better strategy for their own enterprises. On the other hand, to raise awareness of the evaluation framework for the deployment of machine learning systems, to have more comprehensive and useful evaluation metrics (e.g. table 2), rather than only focusing on a single factor (e.g. company cost). This is especially important for decision-makers in the industry.
QFT: Post-training quantization via fast joint finetuning of all degrees of freedom
Authors: Alex Finkelstein, Ella Fuchs, Idan Tal, Mark Grobman, Niv Vosco, Eldad Meller
Date: 2022-12-05 00:00:00
Tasks: All, Quantization

The post-training quantization (PTQ) challenge of bringing quantized neural net accuracy close to original has drawn much attention driven by industry demand. Many of the methods emphasize optimization of a specific degree-of-freedom (DoF), such as quantization step size, preconditioning factors, bias fixing, often chained to others in multi-step solutions. Here we rethink quantized network parameterization in HW-aware fashion, towards a unified analysis of all quantization DoF, permitting for the first time their joint end-to-end finetuning. Our single-step simple and extendable method, dubbed quantization-aware finetuning (QFT), achieves 4-bit weight quantization results on-par with SoTA within PTQ constraints of speed and resource.