pwc-archive/papers-with-abstracts huggingface dataset:
Event-based Shape from Polarization
Authors: Manasi Muglikar, Leonard Bauersfeld, Diederik Paul Moeys, Davide Scaramuzza
Date: 2023-01-17 00:00:00
Proceeding: CVPR 2023 1

State-of-the-art solutions for Shape-from-Polarization (SfP) suffer from a speed-resolution tradeoff: they either sacrifice the number of polarization angles measured or necessitate lengthy acquisition times due to framerate constraints, thus compromising either accuracy or latency. We tackle this tradeoff using event cameras. Event cameras operate at microseconds resolution with negligible motion blur, and output a continuous stream of events that precisely measures how light changes over time asynchronously. We propose a setup that consists of a linear polarizer rotating at high-speeds in front of an event camera. Our method uses the continuous event stream caused by the rotation to reconstruct relative intensities at multiple polarizer angles. Experiments demonstrate that our method outperforms physics-based baselines using frames, reducing the MAE by 25% in synthetic and real-world dataset. In the real world, we observe, however, that the challenging conditions (i.e., when few events are generated) harm the performance of physics-based solutions. To overcome this, we propose a learning-based approach that learns to estimate surface normals even at low event-rates, improving the physics-based approach by 52% on the real world dataset. The proposed system achieves an acquisition speed equivalent to 50 fps (>twice the framerate of the commercial polarization sensor) while retaining the spatial resolution of 1MP. Our evaluation is based on the first large-scale dataset for event-based SfP
Building Scalable Video Understanding Benchmarks through Sports
Authors: Aniket Agarwal, Alex Zhang, Karthik Narasimhan, Igor Gilitschenski, Vishvak Murahari, Yash Kant
Date: 2023-01-17 00:00:00
Tasks: Video Understanding

Existing benchmarks for evaluating long video understanding falls short on two critical aspects, either lacking in scale or quality of annotations. These limitations arise from the difficulty in collecting dense annotations for long videos, which often require manually labeling each frame. In this work, we introduce an automated Annotation and Video Stream Alignment Pipeline (abbreviated ASAP). We demonstrate the generality of ASAP by aligning unlabeled videos of four different sports with corresponding freely available dense web annotations (i.e. commentary). We then leverage ASAP scalability to create LCric, a large-scale long video understanding benchmark, with over 1000 hours of densely annotated long Cricket videos (with an average sample length of ~50 mins) collected at virtually zero annotation cost. We benchmark and analyze state-of-the-art video understanding models on LCric through a large set of compositional multi-choice and regression queries. We establish a human baseline that indicates significant room for new research to explore. Our human studies indicate that ASAP can align videos and annotations with high fidelity, precision, and speed. The dataset along with the code for ASAP and baselines can be accessed here: https://asap-benchmark.github.io/.
SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation
Authors: Junjie Zhou, Yongping Xiong, Chinwai Chiu, Fangyu Liu, Xiangyang Gong
Date: 2023-01-17 00:00:00
Tasks: Point Cloud Segmentation, Semantic Segmentation

Transformer models have achieved promising performances in point cloud segmentation. However, most existing attention schemes provide the same feature learning paradigm for all points equally and overlook the enormous difference in size among scene objects. In this paper, we propose the Size-Aware Transformer (SAT) that can tailor effective receptive fields for objects of different sizes. Our SAT achieves size-aware learning via two steps: introduce multi-scale features to each attention layer and allow each point to choose its attentive fields adaptively. It contains two key designs: the Multi-Granularity Attention (MGA) scheme and the Re-Attention module. The MGA addresses two challenges: efficiently aggregating tokens from distant areas and preserving multi-scale features within one attention layer. Specifically, point-voxel cross attention is proposed to address the first challenge, and the shunted strategy based on the standard multi-head self attention is applied to solve the second. The Re-Attention module dynamically adjusts the attention scores to the fine- and coarse-grained features output by MGA for each point. Extensive experimental results demonstrate that SAT achieves state-of-the-art performances on S3DIS and ScanNetV2 datasets. Our SAT also achieves the most balanced performance on categories among all referred methods, which illustrates the superiority of modelling categories of different sizes. Our code and model will be released after the acceptance of this paper.
Training Methods of Multi-label Prediction Classifiers for Hyperspectral Remote Sensing Images
Authors: Salma Haidar, José Oramas
Date: 2023-01-17 00:00:00
Tasks: Representation Learning

With their combined spectral depth and geometric resolution, hyperspectral remote sensing images embed a wealth of complex, non-linear information that challenges traditional computer vision techniques. Yet, deep learning methods known for their representation learning capabilities prove more suitable for handling such complexities. Unlike applications that focus on single-label, pixel-level classification methods for hyperspectral remote sensing images, we propose a multi-label, patch-level classification method based on a two-component deep-learning network. We use patches of reduced spatial dimension and a complete spectral depth extracted from the remote sensing images. Additionally, we investigate three training schemes for our network: Iterative, Joint, and Cascade. Experiments suggest that the Joint scheme is the best-performing scheme; however, its application requires an expensive search for the best weight combination of the loss constituents. The Iterative scheme enables the sharing of features between the two parts of the network at the early stages of training. It performs better on complex data with multi-labels. Further experiments showed that methods designed with different architectures performed well when trained on patches extracted and labeled according to our sampling method.
BSNet: Lane Detection via Draw B-spline Curves Nearby
Authors: Haoxin Chen, Mengmeng Wang, Yong liu
Date: 2023-01-17 00:00:00
Tasks: Lane Detection

Curve-based methods are one of the classic lane detection methods. They learn the holistic representation of lane lines, which is intuitive and concise. However, their performance lags behind the recent state-of-the-art methods due to the limitation of their lane representation and optimization. In this paper, we revisit the curve-based lane detection methods from the perspectives of the lane representations' globality and locality. The globality of lane representation is the ability to complete invisible parts of lanes with visible parts. The locality of lane representation is the ability to modify lanes locally which can simplify parameter optimization. Specifically, we first propose to exploit the b-spline curve to fit lane lines since it meets the locality and globality. Second, we design a simple yet efficient network BSNet to ensure the acquisition of global and local features. Third, we propose a new curve distance to make the lane detection optimization objective more reasonable and alleviate ill-conditioned problems. The proposed methods achieve state-of-the-art performance on the Tusimple, CULane, and LLAMAS datasets, which dramatically improved the accuracy of curve-based methods in the lane detection task while running far beyond real-time (197FPS).
The use of Octree in point cloud analysis with application to cultural heritage
Authors: Rafał Bieńkowski, Krzysztof E. Rutkowski
Date: 2022-12-12 00:00:00

In this article we present the effects of our work on the subject of the technical approach to the 3D point cloud data analysis through the use of the Octree method to compress, analyse and compute the initial data.
DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking
Authors: Yu Gao, Xi Xu, Tianji Jiang, Siyuan Chen, Yi Yang, Yufeng Yue, Mengyin Fu
Date: 2023-01-17 00:00:00
Tasks: 2D Object Detection, Autonomous Driving, Dimensionality Reduction, Object, object-detection, Object Detection, Pose Estimation

Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimensional information to supervise lower-dimensional tasks is a feasible way to reduce datasets size. In this work, the DR-WLC, a dimensionality reduction cognitive model, which can perform both object detection and pose estimation tasks at the same time is proposed. The model only requires 3D model of objects and unlabeled environment images (with or without objects) to finish the training. In addition, a bounding boxes generation strategy is also proposed to build the relationship between 3D model and 2D object detection task. Experiments show that our method can qualify the work without any manual annotations and it is easy to deploy for practical applications. Source code is at https://github.com/IN2-ViAUn/DR-WLC.
RILS: Masked Visual Reconstruction in Language Semantic Space
Authors: Shusheng Yang, Yixiao Ge, Kun Yi, Dian Li, Ying Shan, XiaoHu Qie, Xinggang Wang
Date: 2023-01-17 00:00:00
Proceeding: CVPR 2023 1
Tasks: Sentence

Both masked image modeling (MIM) and natural language supervision have facilitated the progress of transferable visual pre-training. In this work, we seek the synergy between two paradigms and study the emerging properties when MIM meets natural language supervision. To this end, we present a novel masked visual Reconstruction In Language semantic Space (RILS) pre-training framework, in which sentence representations, encoded by the text encoder, serve as prototypes to transform the vision-only signals into patch-sentence probabilities as semantically meaningful MIM reconstruction targets. The vision models can therefore capture useful components with structured information by predicting proper semantic of masked tokens. Better visual representations could, in turn, improve the text encoder via the image-text alignment objective, which is essential for the effective MIM target transformation. Extensive experimental results demonstrate that our method not only enjoys the best of previous MIM and CLIP but also achieves further improvements on various tasks due to their mutual benefits. RILS exhibits advanced transferability on downstream classification, detection, and segmentation, especially for low-shot regimes. Code will be made available at https://github.com/hustvl/RILS.
Long Range Pooling for 3D Large-Scale Scene Understanding
Authors: Xiang-Li Li, Meng-Hao Guo, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu
Date: 2023-01-17 00:00:00
Proceeding: CVPR 2023 1
Tasks: Scene Understanding

Inspired by the success of recent vision transformers and large kernel design in convolutional neural networks (CNNs), in this paper, we analyze and explore essential reasons for their success. We claim two factors that are critical for 3D large-scale scene understanding: a larger receptive field and operations with greater non-linearity. The former is responsible for providing long range contexts and the latter can enhance the capacity of the network. To achieve the above properties, we propose a simple yet effective long range pooling (LRP) module using dilation max pooling, which provides a network with a large adaptive receptive field. LRP has few parameters, and can be readily added to current CNNs. Also, based on LRP, we present an entire network architecture, LRPNet, for 3D understanding. Ablation studies are presented to support our claims, and show that the LRP module achieves better results than large kernel convolution yet with reduced computation, due to its nonlinearity. We also demonstrate the superiority of LRPNet on various benchmarks: LRPNet performs the best on ScanNet and surpasses other CNN-based methods on S3DIS and Matterport3D. Code will be made publicly available.
Vision Based Machine Learning Algorithms for Out-of-Distribution Generalisation
Authors: Hamza Riaz, Alan F. Smeaton
Date: 2023-01-17 00:00:00
Tasks: Deep Learning, Domain Adaptation, object-detection, Object Detection, Semantic Segmentation, Transfer Learning

There are many computer vision applications including object segmentation, classification, object detection, and reconstruction for which machine learning (ML) shows state-of-the-art performance. Nowadays, we can build ML tools for such applications with real-world accuracy. However, each tool works well within the domain in which it has been trained and developed. Often, when we train a model on a dataset in one specific domain and test on another unseen domain known as an out of distribution (OOD) dataset, models or ML tools show a decrease in performance. For instance, when we train a simple classifier on real-world images and apply that model on the same classes but with a different domain like cartoons, paintings or sketches then the performance of ML tools disappoints. This presents serious challenges of domain generalisation (DG), domain adaptation (DA), and domain shifting. To enhance the power of ML tools, we can rebuild and retrain models from scratch or we can perform transfer learning. In this paper, we present a comparison study between vision-based technologies for domain-specific and domain-generalised methods. In this research we highlight that simple convolutional neural network (CNN) based deep learning methods perform poorly when they have to tackle domain shifting. Experiments are conducted on two popular vision-based benchmarks, PACS and Office-Home. We introduce an implementation pipeline for domain generalisation methods and conventional deep learning models. The outcome confirms that CNN-based deep learning models show poor generalisation compare to other extensive methods.
Opti-CAM: Optimizing saliency maps for interpretability
Authors: Hanwei Zhang, Felipe Torres, Ronan Sicre, Yannis Avrithis, Stephane Ayache
Date: 2023-01-17 00:00:00

Methods based on class activation maps (CAM) provide a simple mechanism to interpret predictions of convolutional neural networks by using linear combinations of feature maps as saliency maps. By contrast, masking-based methods optimize a saliency map directly in the image space or learn it by training another network on additional data. In this work we introduce Opti-CAM, combining ideas from CAM-based and masking-based approaches. Our saliency map is a linear combination of feature maps, where weights are optimized per image such that the logit of the masked image for a given class is maximized. We also fix a fundamental flaw in two of the most common evaluation metrics of attribution methods. On several datasets, Opti-CAM largely outperforms other CAM-based approaches according to the most relevant classification metrics. We provide empirical evidence supporting that localization and classifier interpretability are not necessarily aligned.
Preserving Privacy in Surgical Video Analysis Using Artificial Intelligence: A Deep Learning Classifier to Identify Out-of-Body Scenes in Endoscopic Videos
Authors: Joël L. Lavanchy, Armine Vardazaryan, Pietro Mascagni, AI4SafeChole Consortium, Didier Mutter, Nicolas Padoy
Date: 2023-01-17 00:00:00

Objective: To develop and validate a deep learning model for the identification of out-of-body images in endoscopic videos. Background: Surgical video analysis facilitates education and research. However, video recordings of endoscopic surgeries can contain privacy-sensitive information, especially if out-of-body scenes are recorded. Therefore, identification of out-of-body scenes in endoscopic videos is of major importance to preserve the privacy of patients and operating room staff. Methods: A deep learning model was trained and evaluated on an internal dataset of 12 different types of laparoscopic and robotic surgeries. External validation was performed on two independent multicentric test datasets of laparoscopic gastric bypass and cholecystectomy surgeries. All images extracted from the video datasets were annotated as inside or out-of-body. Model performance was evaluated compared to human ground truth annotations measuring the receiver operating characteristic area under the curve (ROC AUC). Results: The internal dataset consisting of 356,267 images from 48 videos and the two multicentric test datasets consisting of 54,385 and 58,349 images from 10 and 20 videos, respectively, were annotated. Compared to ground truth annotations, the model identified out-of-body images with 99.97% ROC AUC on the internal test dataset. Mean $\pm$ standard deviation ROC AUC on the multicentric gastric bypass dataset was 99.94$\pm$0.07% and 99.71$\pm$0.40% on the multicentric cholecystectomy dataset, respectively. Conclusion: The proposed deep learning model can reliably identify out-of-body images in endoscopic videos. The trained model is publicly shared. This facilitates privacy preservation in surgical video analysis.
Learning Audio-Driven Viseme Dynamics for 3D Face Animation
Authors: Linchao Bao, Haoxian Zhang, Yue Qian, Tangli Xue, Changhai Chen, Xuefei Zhe, Di Kang
Date: 2023-01-15 00:00:00
Tasks: 3D Face Animation

We present a novel audio-driven facial animation approach that can generate realistic lip-synchronized 3D facial animations from the input audio. Our approach learns viseme dynamics from speech videos, produces animator-friendly viseme curves, and supports multilingual speech inputs. The core of our approach is a novel parametric viseme fitting algorithm that utilizes phoneme priors to extract viseme parameters from speech videos. With the guidance of phonemes, the extracted viseme curves can better correlate with phonemes, thus more controllable and friendly to animators. To support multilingual speech inputs and generalizability to unseen voices, we take advantage of deep audio feature models pretrained on multiple languages to learn the mapping from audio to viseme curves. Our audio-to-curves mapping achieves state-of-the-art performance even when the input audio suffers from distortions of volume, pitch, speed, or noise. Lastly, a viseme scanning approach for acquiring high-fidelity viseme assets is presented for efficient speech animation production. We show that the predicted viseme curves can be applied to different viseme-rigged characters to yield various personalized animations with realistic and natural facial motions. Our approach is artist-friendly and can be easily integrated into typical animation production workflows including blendshape or bone based animation.
Learning to adapt unknown noise for hyperspectral image denoising
Authors: Xiangyu Rui, Xiangyong Cao, Jun Shu, Qian Zhao, Deyu Meng
Date: 2022-12-09 00:00:00
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

For hyperspectral image (HSI) denoising task, the causes of noise embeded in an HSI are typically complex and uncontrollable. Thus, it remains a challenge for model-based HSI denoising methods to handle complex noise. To enhance the noise-handling capabilities of existing model-based methods, we resort to design a general weighted data fidelity term. The weight in this term is used to assess the noise intensity and thus elementwisely adjust the contribution of the observed noisy HSI in a denoising model. The similar concept of "weighting" has been hinted in several methods. Due to the unknown nature of the noise distribution, the implementation of "weighting" in these works are usually achieved via empirical formula for specific denoising method. In this work, we propose to predict the weight by a hyper-weight network (i.e., HWnet). The HWnet is learned exactly from several model-based HSI denoising methods in a bi-level optimization framework based on the data-driven methodology. For a noisy HSI, the learned HWnet outputs its corresponding weight. Then the weighted data fidelity term implemented with the predicted weight can be explicitly combined with a target model-based HSI denoising method. In this way, our HWnet achieves the goal of enhancing the noise adaptation ability of model-based HSI denoising methods for different noisy HSIs. Extensive experiments verify that the proposed HWnet can effecitvely help to improve the ability of an HSI denoising model to handle different complex noises. This further implies that our HWnet could transfer the noise knowledge at the model level and we also study the corresponding generalization theory for simple illustration.
Improving Reliability of Fine-tuning with Block-wise Optimisation
Authors: Basel Barakat, Qiang Huang
Date: 2023-01-15 00:00:00

Finetuning can be used to tackle domain-specific tasks by transferring knowledge. Previous studies on finetuning focused on adapting only the weights of a task-specific classifier or re-optimizing all layers of the pre-trained model using the new task data. The first type of methods cannot mitigate the mismatch between a pre-trained model and the new task data, and the second type of methods easily cause over-fitting when processing tasks with limited data. To explore the effectiveness of fine-tuning, we propose a novel block-wise optimization mechanism, which adapts the weights of a group of layers of a pre-trained model. In our work, the layer selection can be done in four different ways. The first is layer-wise adaptation, which aims to search for the most salient single layer according to the classification performance. The second way is based on the first one, jointly adapting a small number of top-ranked layers instead of using an individual layer. The third is block based segmentation, where the layers of a deep network is segmented into blocks by non-weighting layers, such as the MaxPooling layer and Activation layer. The last one is to use a fixed-length sliding window to group layers block by block. To identify which group of layers is the most suitable for finetuning, the search starts from the target end and is conducted by freezing other layers excluding the selected layers and the classification layers. The most salient group of layers is determined in terms of classification performance. In our experiments, the proposed approaches are tested on an often-used dataset, Tf_flower, by finetuning five typical pre-trained models, VGG16, MobileNet-v1, MobileNet-v2, MobileNet-v3, and ResNet50v2, respectively. The obtained results show that the use of our proposed block-wise approaches can achieve better performances than the two baseline methods and the layer-wise method.