pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting map information for self-supervised learning in motion forecasting
Authors: Caio Azevedo, Thomas Gilles, Stefano Sabatini, Dzmitry Tsishkou
Date: 2022-10-10 00:00:00
Tasks: Motion Forecasting, Self-Supervised Learning, Trajectory Prediction

Inspired by recent developments regarding the application of self-supervised learning (SSL), we devise an auxiliary task for trajectory prediction that takes advantage of map-only information such as graph connectivity with the intent of improving map comprehension and generalization. We apply this auxiliary task through two frameworks - multitasking and pretraining. In either framework we observe significant improvement of our baseline in metrics such as $\mathrm{minFDE}_6$ (as much as 20.3%) and $\mathrm{MissRate}_6$ (as much as 33.3%), as well as a richer comprehension of map features demonstrated by different training configurations. The results obtained were consistent in all three data sets used for experiments: Argoverse, Interaction and NuScenes. We also submit our new pretrained model's results to the Interaction challenge and achieve $\textit{1st}$ place with respect to $\mathrm{minFDE}_6$ and $\mathrm{minADE}_6$.
TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment
Authors: Yujie Zhang, Qi Yang, Yifei Zhou, Xiaozhong Xu, Le Yang, Yiling Xu
Date: 2022-10-10 00:00:00
Tasks: Point Cloud Quality Assessment

The goal of objective point cloud quality assessment (PCQA) research is to develop quantitative metrics that measure point cloud quality in a perceptually consistent manner. Merging the research of cognitive science and intuition of the human visual system (HVS), in this paper, we evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference, which in practice can be approximated by the code length of one point cloud when the other is given. For this purpose, we first make space segmentation for the reference and distorted point clouds based on a 3D Voronoi diagram to obtain a series of local patch pairs. Next, inspired by the predictive coding theory, we utilize a space-aware vector autoregressive (SA-VAR) model to encode the geometry and color channels of each reference patch with and without the distorted patch, respectively. Assuming that the residual errors follow the multi-variate Gaussian distributions, the self-complexity of the reference and transformational complexity between the reference and distorted samples are computed using covariance matrices. Additionally, the prediction terms generated by SA-VAR are introduced as one auxiliary feature to promote the final quality prediction. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases. The results demonstrate that TCDM achieves state-of-the-art (SOTA) performance, and further analysis confirms its robustness in various scenarios. The code is publicly available at https://github.com/zyj1318053/TCDM.
A CNN Based Approach for the Point-Light Photometric Stereo Problem
Authors: Fotios Logothetis, Roberto Mecca, Ignas Budvytis, Roberto Cipolla
Date: 2022-10-10 00:00:00

Reconstructing the 3D shape of an object using several images under different light sources is a very challenging task, especially when realistic assumptions such as light propagation and attenuation, perspective viewing geometry and specular light reflection are considered. Many of works tackling Photometric Stereo (PS) problems often relax most of the aforementioned assumptions. Especially they ignore specular reflection and global illumination effects. In this work, we propose a CNN-based approach capable of handling these realistic assumptions by leveraging recent improvements of deep neural networks for far-field Photometric Stereo and adapt them to the point light setup. We achieve this by employing an iterative procedure of point-light PS for shape estimation which has two main steps. Firstly we train a per-pixel CNN to predict surface normals from reflectance samples. Secondly, we compute the depth by integrating the normal field in order to iteratively estimate light directions and attenuation which is used to compensate the input images to compute reflectance samples for the next iteration. Our approach sigificantly outperforms the state-of-the-art on the DiLiGenT real world dataset. Furthermore, in order to measure the performance of our approach for near-field point-light source PS data, we introduce LUCES the first real-world 'dataset for near-fieLd point light soUrCe photomEtric Stereo' of 14 objects of different materials were the effects of point light sources and perspective viewing are a lot more significant. Our approach also outperforms the competition on this dataset as well. Data and test code are available at the project page.
Association Graph Learning for Multi-Task Classification with Category Shifts
Authors: Jiayi Shen, Zehao Xiao, XianTong Zhen, Cees G. M. Snoek, Marcel Worring
Date: 2022-10-10 00:00:00
Tasks: General Classification, Graph Learning, Lesion Classification, Skin Lesion Classification, Transfer Learning

In this paper, we focus on multi-task classification, where related classification tasks share the same label space and are learned simultaneously. In particular, we tackle a new setting, which is more realistic than currently addressed in the literature, where categories shift from training to test data. Hence, individual tasks do not contain complete training data for the categories in the test set. To generalize to such test data, it is crucial for individual tasks to leverage knowledge from related tasks. To this end, we propose learning an association graph to transfer knowledge among tasks for missing classes. We construct the association graph with nodes representing tasks, classes and instances, and encode the relationships among the nodes in the edges to guide their mutual knowledge transfer. By message passing on the association graph, our model enhances the categorical information of each instance, making it more discriminative. To avoid spurious correlations between task and class nodes in the graph, we introduce an assignment entropy maximization that encourages each class node to balance its edge weights. This enables all tasks to fully utilize the categorical information from related tasks. An extensive evaluation on three general benchmarks and a medical dataset for skin lesion classification reveals that our method consistently performs better than representative baselines.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Authors: Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers, Felix Grimberg, Chaoyang He, Regis Loeb, Paul Mangold, Tanguy Marchand, Othmane Marfoq, Erum Mushtaq, Boris Muzellec, Constantin Philippenko, Santiago Silva, Maria Teleńczuk, Shadi Albarqouni, Salman Avestimehr, Aurélien Bellet, Aymeric Dieuleveut, Martin Jaggi, Sai Praneeth Karimireddy, Marco Lorenzi, Giovanni Neglia, Marc Tommasi, Mathieu Andreux
Date: 2022-10-10 00:00:00
Tasks: Federated Learning

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable clients, each holding medium to large datasets, and is typically found in applications such as healthcare, finance, or industry. While previous works have proposed representative datasets for cross-device FL, few realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic research in this critical application. In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL. FLamby encompasses 7 healthcare datasets with natural splits, covering multiple tasks, modalities, and data volumes, each accompanied with baseline training code. As an illustration, we additionally benchmark standard FL algorithms on all datasets. Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research. FLamby is available at~\url{www.github.com/owkin/flamby}.
Universal Adversarial Perturbations: Efficiency on a small image dataset
Authors: Waris Radji
Date: 2022-10-10 00:00:00
Tasks: image-classification, Image Classification

Although neural networks perform very well on the image classification task, they are still vulnerable to adversarial perturbations that can fool a neural network without visibly changing an input image. A paper has shown the existence of Universal Adversarial Perturbations which when added to any image will fool the neural network with a very high probability. In this paper we will try to reproduce the experience of the Universal Adversarial Perturbations paper, but on a smaller neural network architecture and training set, in order to be able to study the efficiency of the computed perturbation.
Is your noise correction noisy? PLS: Robustness to label noise with two stage detection
Authors: Paul Albert, Eric Arazo, Tarun Krishna, Noel E. O'Connor, Kevin McGuinness
Date: 2022-10-10 00:00:00
Tasks: Pseudo Label

Designing robust algorithms capable of training accurate neural networks on uncurated datasets from the web has been the subject of much research as it reduces the need for time consuming human labor. The focus of many previous research contributions has been on the detection of different types of label noise; however, this paper proposes to improve the correction accuracy of noisy samples once they have been detected. In many state-of-the-art contributions, a two phase approach is adopted where the noisy samples are detected before guessing a corrected pseudo-label in a semi-supervised fashion. The guessed pseudo-labels are then used in the supervised objective without ensuring that the label guess is likely to be correct. This can lead to confirmation bias, which reduces the noise robustness. Here we propose the pseudo-loss, a simple metric that we find to be strongly correlated with pseudo-label correctness on noisy samples. Using the pseudo-loss, we dynamically down weight under-confident pseudo-labels throughout training to avoid confirmation bias and improve the network accuracy. We additionally propose to use a confidence guided contrastive objective that learns robust representation on an interpolated objective between class bound (supervised) for confidently corrected samples and unsupervised representation for under-confident label corrections. Experiments demonstrate the state-of-the-art performance of our Pseudo-Loss Selection (PLS) algorithm on a variety of benchmark datasets including curated data synthetically corrupted with in-distribution and out-of-distribution noise, and two real world web noise datasets. Our experiments are fully reproducible github.com/PaulAlbert31/SNCF
ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection
Authors: Rebbapragada V C Sairam, Monish Keswani, Uttaran Sinha, Nishit Shah, Vineeth N Balasubramanian
Date: 2022-10-10 00:00:00
Tasks: Object, object-detection, Object Detection

Deep neural networks tend to reciprocate the bias of their training dataset. In object detection, the bias exists in the form of various imbalances such as class, background-foreground, and object size. In this paper, we denote size of an object as the number of pixels it covers in an image and size imbalance as the over-representation of certain sizes of objects in a dataset. We aim to address the problem of size imbalance in drone-based aerial image datasets. Existing methods for solving size imbalance are based on architectural changes that utilize multiple scales of images or feature maps for detecting objects of different sizes. We, on the other hand, propose a novel ARchitectUre-agnostic BAlanced Loss (ARUBA) that can be applied as a plugin on top of any object detection model. It follows a neighborhood-driven approach inspired by the ordinality of object size. We evaluate the effectiveness of our approach through comprehensive experiments on aerial datasets such as HRSC2016, DOTAv1.0, DOTAv1.5 and VisDrone and obtain consistent improvement in performance.
Floorplan-Aware Camera Poses Refinement
Authors: Anna Sokolova, Filipp Nikitin, Anna Vorontsova, Anton Konushin
Date: 2022-10-10 00:00:00
Tasks: 3D Reconstruction, Model Optimization

Processing large indoor scenes is a challenging task, as scan registration and camera trajectory estimation methods accumulate errors across time. As a result, the quality of reconstructed scans is insufficient for some applications, such as visual-based localization and navigation, where the correct position of walls is crucial. For many indoor scenes, there exists an image of a technical floorplan that contains information about the geometry and main structural elements of the scene, such as walls, partitions, and doors. We argue that such a floorplan is a useful source of spatial information, which can guide a 3D model optimization. The standard RGB-D 3D reconstruction pipeline consists of a tracking module applied to an RGB-D sequence and a bundle adjustment (BA) module that takes the posed RGB-D sequence and corrects the camera poses to improve consistency. We propose a novel optimization algorithm expanding conventional BA that leverages the prior knowledge about the scene structure in the form of a floorplan. Our experiments on the Redwood dataset and our self-captured data demonstrate that utilizing floorplan improves accuracy of 3D reconstructions.
The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization
Authors: Luca Bonfiglioli, Marco Toschi, Davide Silvestri, Nicola Fioraio, Daniele De Gregorio
Date: 2022-10-10 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization. Photo-realistic images of procedurally generated candies are rendered in a controlled environment under multiple lightning conditions, also providing depth and normal maps in an industrial conveyor scenario. We make available anomaly-free samples for model training and validation, while anomalous instances with precise ground-truth annotations are provided only in the test set. The dataset comprises ten classes of candies, each showing different challenges, such as complex textures, self-occlusions and specularities. Furthermore, we achieve large intra-class variation by randomly drawing key parameters of a procedural rendering pipeline, which enables the creation of an arbitrary number of instances with photo-realistic appearance. Likewise, anomalies are injected into the rendering graph and pixel-wise annotations are automatically generated, overcoming human-biases and possible inconsistencies. We believe this dataset may encourage the exploration of original approaches to solve the anomaly detection task, e.g. by combining color, depth and normal maps, as they are not provided by most of the existing datasets. Indeed, in order to demonstrate how exploiting additional information may actually lead to higher detection performance, we show the results obtained by training a deep convolutional autoencoder to reconstruct different combinations of inputs.
BoundaryFace: A mining framework with noise label self-correction for Face Recognition
Authors: Shijie Wu, Xun Gong
Date: 2022-10-10 00:00:00
Tasks: Face Recognition

Face recognition has made tremendous progress in recent years due to the advances in loss functions and the explosive growth in training sets size. A properly designed loss is seen as key to extract discriminative features for classification. Several margin-based losses have been proposed as alternatives of softmax loss in face recognition. However, two issues remain to consider: 1) They overlook the importance of hard sample mining for discriminative learning. 2) Label noise ubiquitously exists in large-scale datasets, which can seriously damage the model's performance. In this paper, starting from the perspective of decision boundary, we propose a novel mining framework that focuses on the relationship between a sample's ground truth class center and its nearest negative class center. Specifically, a closed-set noise label self-correction module is put forward, making this framework work well on datasets containing a lot of label noise. The proposed method consistently outperforms SOTA methods in various face recognition benchmarks. Training code has been released at https://github.com/SWJTU-3DVision/BoundaryFace.
Using Detection, Tracking and Prediction in Visual SLAM to Achieve Real-time Semantic Mapping of Dynamic Scenarios
Authors: Xingyu Chen, Jianru Xue, Jianwu Fang, Yuxin Pan, Nanning Zheng
Date: 2022-10-10 00:00:00
Tasks: CPU, Object, object-detection, Object Detection

In this paper, we propose a lightweight system, RDS-SLAM, based on ORB-SLAM2, which can accurately estimate poses and build semantic maps at object level for dynamic scenarios in real time using only one commonly used Intel Core i7 CPU. In RDS-SLAM, three major improvements, as well as major architectural modifications, are proposed to overcome the limitations of ORB-SLAM2. Firstly, it adopts a lightweight object detection neural network in key frames. Secondly, an efficient tracking and prediction mechanism is embedded into the system to remove the feature points belonging to movable objects in all incoming frames. Thirdly, a semantic octree map is built by probabilistic fusion of detection and tracking results, which enables a robot to maintain a semantic description at object level for potential interactions in dynamic scenarios. We evaluate RDS-SLAM in TUM RGB-D dataset, and experimental results show that RDS-SLAM can run with 30.3 ms per frame in dynamic scenarios using only an Intel Core i7 CPU, and achieves comparable accuracy compared with the state-of-the-art SLAM systems which heavily rely on both Intel Core i7 CPUs and powerful GPUs.
Visually Similar Products Retrieval for Shopsy
Authors: Prajit Nadkarni, Narendra Varma Dasararaju
Date: 2022-10-10 00:00:00
Tasks: Attribute, Image Compression, Multi-Task Learning, Retrieval, Triplet

Visual search is of great assistance in reseller commerce, especially for non-tech savvy users with affinity towards regional languages. It allows resellers to accurately locate the products that they seek, unlike textual search which recommends products from head brands. Product attributes available in e-commerce have a great potential for building better visual search systems as they capture fine grained relations between data points. In this work, we design a visual search system for reseller commerce using a multi-task learning approach. We also highlight and address the challenges like image compression, cropping, scribbling on the image, etc, faced in reseller commerce. Our model consists of three different tasks: attribute classification, triplet ranking and variational autoencoder (VAE). Masking technique is used for designing the attribute classification. Next, we introduce an offline triplet mining technique which utilizes information from multiple attributes to capture relative order within the data. This technique displays a better performance compared to the traditional triplet mining baseline, which uses single label/attribute information. We also compare and report incremental gain achieved by our unified multi-task model over each individual task separately. The effectiveness of our method is demonstrated using the in-house dataset of product images from the Lifestyle business-unit of Flipkart, India's largest e-commerce company. To efficiently retrieve the images in production, we use the Approximate Nearest Neighbor (ANN) index. Finally, we highlight our production environment constraints and present the design choices and experiments conducted to select a suitable ANN index.
CLIP-Diffusion-LM: Apply Diffusion Model on Image Captioning
Authors: Shitong Xu
Date: 2022-10-10 00:00:00
Tasks: Decoder, Denoising, Image Captioning, Image Generation, Text Generation

Image captioning task has been extensively researched by previous work. However, limited experiments focus on generating captions based on non-autoregressive text decoder. Inspired by the recent success of the denoising diffusion model on image synthesis tasks, we apply denoising diffusion probabilistic models to text generation in image captioning tasks. We show that our CLIP-Diffusion-LM is capable of generating image captions using significantly fewer inference steps than autoregressive models. On the Flickr8k dataset, the model achieves 0.1876 BLEU-4 score. By training on the combined Flickr8k and Flickr30k dataset, our model achieves 0.2470 BLEU-4 score. Our code is available at https://github.com/xu-shitong/diffusion-image-captioning.
Comparing the carbon costs and benefits of low-resource solar nowcasting
Authors: Ben Dixon, María Pérez-Ortiz, Jacob Bieker
Date: 2022-10-10 00:00:00

Solar PV yield nowcasting is used to help anticipate peaks and troughs in demand to support grid integration. This paper compares multiple low-resource approaches to nowcasting solar PV yield, using a dataset of UK satellite imagery and solar PV energy readings over a 1 to 4-hour time range. The paper also estimates the carbon emissions generated and averted by deploying models, and finds that even small models that could be deployable in low-resource settings may have a benefit several orders of magnitude greater than its carbon cost. The paper also examines prediction errors and the activations in a CNN.
SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene Reconstruction
Authors: Yitong Xia, Hao Tang, Radu Timofte, Luc van Gool
Date: 2022-10-10 00:00:00
Tasks: Image Generation, NeRF, Pose Estimation

NeRFmm is the Neural Radiance Fields (NeRF) that deal with Joint Optimization tasks, i.e., reconstructing real-world scenes and registering camera parameters simultaneously. Despite NeRFmm producing precise scene synthesis and pose estimations, it still struggles to outperform the full-annotated baseline on challenging scenes. In this work, we identify that there exists a systematic sub-optimality in joint optimization and further identify multiple potential sources for it. To diminish the impacts of potential sources, we propose Sinusoidal Neural Radiance Fields (SiNeRF) that leverage sinusoidal activations for radiance mapping and a novel Mixed Region Sampling (MRS) for selecting ray batch efficiently. Quantitative and qualitative results show that compared to NeRFmm, SiNeRF achieves comprehensive significant improvements in image synthesis quality and pose estimation accuracy. Codes are available at https://github.com/yitongx/sinerf.