pwc-archive/papers-with-abstracts huggingface dataset:
Joint Learning of Salient Object Detection, Depth Estimation and Contour Extraction
Authors: Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu
Date: 2022-03-09 00:00:00
Tasks: Depth Estimation, object-detection, Object Detection, RGB-D Salient Object Detection, Salient Object Detection

Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/MMFT.
Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity
Authors: Sumit Shekhar, Max Reimann, Amir Semmo, Sebastian Pasewaldt, Jürgen Döllner, Matthias Trapp
Date: 2022-03-09 00:00:00
Tasks: Video Enhancement

Image acquisition in low-light conditions suffers from poor quality and significant degradation in visual aesthetics. This affects the visual perception of the acquired image and the performance of various computer vision and image processing algorithms applied after acquisition. Especially for videos, the additional temporal domain makes it more challenging, wherein we need to preserve quality in a temporally coherent manner. We present a simple yet effective approach for low-light image and video enhancement. To this end, we introduce "Adaptive Chromaticity", which refers to an adaptive computation of image chromaticity. The above adaptivity allows us to avoid the costly step of low-light image decomposition into illumination and reflectance, employed by many existing techniques. All stages in our method consist of only point-based operations and high-pass or low-pass filtering, thereby ensuring that the amount of temporal incoherence is negligible when applied on a per-frame basis for videos. Our results on standard lowlight image datasets show the efficacy of our algorithm and its qualitative and quantitative superiority over several state-of-the-art techniques. For videos captured in the wild, we perform a user study to demonstrate the preference for our method in comparison to state-of-the-art approaches.
Efficient Image Representation Learning with Federated Sampled Softmax
Authors: Sagar M. Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, Tomer Meron
Date: 2022-03-09 00:00:00
Tasks: Descriptive, Federated Learning, Representation Learning

Learning image representations on decentralized data can bring many benefits in cases where data cannot be aggregated across data silos. Softmax cross entropy loss is highly effective and commonly used for learning image representations. Using a large number of classes has proven to be particularly beneficial for the descriptive power of such representations in centralized learning. However, doing so on decentralized data with Federated Learning is not straightforward as the demand on FL clients' computation and communication increases proportionally to the number of classes. In this work we introduce federated sampled softmax (FedSS), a resource-efficient approach for learning image representation with Federated Learning. Specifically, the FL clients sample a set of classes and optimize only the corresponding model parameters with respect to a sampled softmax objective that approximates the global full softmax objective. We examine the loss formulation and empirically show that our method significantly reduces the number of parameters transferred to and optimized by the client devices, while performing on par with the standard full softmax method. This work creates a possibility for efficiently learning image representations on decentralized data with a large number of classes under the federated setting.
VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction
Authors: A. Konrad, J. McDonald, R. Villing
Date: 2022-03-09 00:00:00
Tasks: CPU

We present the Versatile Grasp Quality Convolutional Neural Network (VGQ-CNN), a grasp quality prediction network for 6-DOF grasps. VGQ-CNN can be used when evaluating grasps for objects seen from a wide range of camera poses or mobile robots without the need to retrain the network. By defining the grasp orientation explicitly as an input to the network, VGQ-CNN can evaluate 6-DOF grasp poses, moving beyond the 4-DOF grasps used in most image-based grasp evaluation methods like GQ-CNN. To train VGQ-CNN, we generate the new Versatile Grasp dataset (VG-dset) containing 6-DOF grasps observed from a wide range of camera poses. VGQ-CNN achieves a balanced accuracy of 82.1% on our test-split while generalising to a variety of camera poses. Meanwhile, it achieves competitive performance for overhead cameras and top-grasps with a balanced accuracy of 74.2% compared to GQ-CNN's 76.6%. We also propose a modified network architecture, FAST-VGQ-CNN, that speeds up inference using a shared encoder architecture and can make 128 grasp quality predictions in 12ms on a CPU. Code and data are available at https://aucoroboticsmu.github.io/vgq-cnn/.
CEU-Net: Ensemble Semantic Segmentation of Hyperspectral Images Using Clustering
Authors: Nicholas Soucy, Salimeh Yasaei Sekeh
Date: 2022-03-09 00:00:00
Tasks: Clustering, Clustering Ensemble, Segmentation, Semantic Segmentation

Most semantic segmentation approaches of Hyperspectral images (HSIs) use and require preprocessing steps in the form of patching to accurately classify diversified land cover in remotely sensed images. These approaches use patching to incorporate the rich neighborhood information in images and exploit the simplicity and segmentability of the most common HSI datasets. In contrast, most landmasses in the world consist of overlapping and diffused classes, making neighborhood information weaker than what is seen in common HSI datasets. To combat this issue and generalize the segmentation models to more complex and diverse HSI datasets, in this work, we propose our novel flagship model: Clustering Ensemble U-Net (CEU-Net). CEU-Net uses the ensemble method to combine spectral information extracted from convolutional neural network (CNN) training on a cluster of landscape pixels. Our CEU-Net model outperforms existing state-of-the-art HSI semantic segmentation methods and gets competitive performance with and without patching when compared to baseline models. We highlight CEU-Net's high performance across Botswana, KSC, and Salinas datasets compared to HybridSN and AeroRIT methods.
Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction
Authors: Yuanhao Cai, Jing Lin, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, Luc van Gool
Date: 2022-03-09 00:00:00
Tasks: Compressive Sensing, Image Reconstruction, Spectral Reconstruction

Many algorithms have been developed to solve the inverse problem of coded aperture snapshot spectral imaging (CASSI), i.e., recovering the 3D hyperspectral images (HSIs) from a 2D compressive measurement. In recent years, learning-based methods have demonstrated promising performance and dominated the mainstream research direction. However, existing CNN-based methods show limitations in capturing long-range dependencies and non-local self-similarity. Previous Transformer-based methods densely sample tokens, some of which are uninformative, and calculate the multi-head self-attention (MSA) between some tokens that are unrelated in content. This does not fit the spatially sparse nature of HSI signals and limits the model scalability. In this paper, we propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST), firstly embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing. Comprehensive experiments show that our CST significantly outperforms state-of-the-art methods while requiring cheaper computational costs. The code and models will be released at https://github.com/caiyuanhao1998/MST
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
Authors: Jiaming Zhang, Huayao Liu, Kailun Yang, Xinxin Hu, Ruiping Liu, Rainer Stiefelhagen
Date: 2022-03-09 00:00:00
Tasks: 3D Object Detection, Autonomous Vehicles, Camouflaged Object Segmentation, Image Manipulation Localization, Image Segmentation, Multispectral Object Detection, Object Detection, Pedestrian Detection, Scene Understanding, Segmentation, Semantic Segmentation, Thermal Image Segmentation

Scene understanding based on image segmentation is a crucial component of autonomous vehicles. Pixel-wise semantic segmentation of RGB images can be advanced by exploiting complementary features from the supplementary modality (X-modality). However, covering a wide variety of sensors with a modality-agnostic model remains an unresolved problem due to variations in sensor characteristics among different modalities. Unlike previous modality-specific methods, in this work, we propose a unified fusion framework, CMX, for RGB-X semantic segmentation. To generalize well across different modalities, that often include supplements as well as uncertainties, a unified cross-modal interaction is crucial for modality fusion. Specifically, we design a Cross-Modal Feature Rectification Module (CM-FRM) to calibrate bi-modal features by leveraging the features from one modality to rectify the features of the other modality. With rectified feature pairs, we deploy a Feature Fusion Module (FFM) to perform sufficient exchange of long-range contexts before mixing. To verify CMX, for the first time, we unify five modalities complementary to RGB, i.e., depth, thermal, polarization, event, and LiDAR. Extensive experiments show that CMX generalizes well to diverse multi-modal fusion, achieving state-of-the-art performances on five RGB-Depth benchmarks, as well as RGB-Thermal, RGB-Polarization, and RGB-LiDAR datasets. Besides, to investigate the generalizability to dense-sparse data fusion, we establish an RGB-Event semantic segmentation benchmark based on the EventScape dataset, on which CMX sets the new state-of-the-art. The source code of CMX is publicly available at https://github.com/huaaaliu/RGBX_Semantic_Segmentation.
A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network
Authors: Xiuyuan Li, Fengchao Li, Jiangang Yu, Guowen An
Date: 2022-03-09 00:00:00
Tasks: Deblurring, Multi-Task Learning, Object, object-detection, Object Detection

Deep learning-based underwater object detection (UOD) remains a major challenge due to the degraded visibility and difficulty to obtain sufficient underwater object images captured from various perspectives for training. To address these issues, this paper presents a high-precision UOD based on joint self-supervised deblurring and improved spatial transformer network. A self-supervised deblurring subnetwork is introduced into the designed multi-task learning aided object detection architecture to force the shared feature extraction module to output clean features for detection subnetwork. Aiming at alleviating the limitation of insufficient photos from different perspectives, an improved spatial transformer network is designed based on perspective transformation, adaptively enriching image features within the network. The experimental results show that the proposed UOD approach achieved 47.9 mAP in URPC2017 and 70.3 mAP in URPC2018, outperforming many state-of-the-art UOD methods and indicating the designed method is more suitable for UOD.
Text-DIAE: A Self-Supervised Degradation Invariant Autoencoders for Text Recognition and Document Enhancement
Authors: Mohamed Ali Souibgui, Sanket Biswas, Andres Mafla, Ali Furkan Biten, Alicia Fornés, Yousri Kessentini, Josep Lladós, Lluis Gomez, Dimosthenis Karatzas
Date: 2022-03-09 00:00:00
Tasks: Document Enhancement, Image Enhancement, Scene Text Recognition

In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement. We start by employing a transformer-based architecture that incorporates three pretext tasks as learning objectives to be optimized during pre-training without the usage of labeled data. Each of the pretext objectives is specifically tailored for the final downstream tasks. We conduct several ablation experiments that confirm the design choice of the selected pretext tasks. Importantly, the proposed model does not exhibit limitations of previous state-of-the-art methods based on contrastive losses, while at the same time requiring substantially fewer data samples to converge. Finally, we demonstrate that our method surpasses the state-of-the-art in existing supervised and self-supervised settings in handwritten and scene text recognition and document image enhancement. Our code and trained models will be made publicly available at~\url{ http://Upon_Acceptance}.
A high-precision self-supervised monocular visual odometry in foggy weather based on robust cycled generative adversarial networks and multi-task learning aided depth estimation
Authors: Xiuyuan Li, Jiangang Yu, Fengchao Li, Guowen An
Date: 2022-03-09 00:00:00
Tasks: Depth Estimation, Generative Adversarial Network, Monocular Visual Odometry, Multi-Task Learning, Pose Estimation, Visual Odometry

This paper proposes a high-precision self-supervised monocular VO, which is specifically designed for navigation in foggy weather. A cycled generative adversarial network is designed to obtain high-quality self-supervised loss via forcing the forward and backward half-cycle to output consistent estimation. Moreover, gradient-based loss and perceptual loss are introduced to eliminate the interference of complex photometric change on self-supervised loss in foggy weather. To solve the ill-posed problem of depth estimation, a self-supervised multi-task learning aided depth estimation module is designed based on the strong correlation between the depth estimation and transmission map calculation of hazy images in foggy weather. The experimental results on the synthetic foggy KITTI dataset show that the proposed self-supervised monocular VO performs better in depth and pose estimation than other state-of-the-art monocular VO in the literature, indicating the designed method is more suitable for foggy weather.
NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation
Authors: Fu Li, Hao Yu, Ivan Shugurov, Benjamin Busam, Shaowu Yang, Slobodan Ilic
Date: 2022-03-09 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, NeRF, Pose Estimation, Semantic Segmentation

Pose estimation of 3D objects in monocular images is a fundamental and long-standing problem in computer vision. Existing deep learning approaches for 6D pose estimation typically rely on the assumption of availability of 3D object models and 6D pose annotations. However, precise annotation of 6D poses in real data is intricate, time-consuming and not scalable, while synthetic data scales well but lacks realism. To avoid these problems, we present a weakly-supervised reconstruction-based pipeline, named NeRF-Pose, which needs only 2D object segmentation and known relative camera poses during training. Following the first-reconstruct-then-regress idea, we first reconstruct the objects from multiple views in the form of an implicit neural representation. Then, we train a pose regression network to predict pixel-wise 2D-3D correspondences between images and the reconstructed model. At inference, the approach only needs a single image as input. A NeRF-enabled PnP+RANSAC algorithm is used to estimate stable and accurate pose from the predicted correspondences. Experiments on LineMod and LineMod-Occlusion show that the proposed method has state-of-the-art accuracy in comparison to the best 6D pose estimation methods in spite of being trained only with weak labels. Besides, we extend the Homebrewed DB dataset with more real training images to support the weakly supervised task and achieve compelling results on this dataset. The extended dataset and code will be released soon.
Evaluation of YOLO Models with Sliced Inference for Small Object Detection
Authors: Muhammed Can Keles, Batuhan Salmanoglu, Mehmet Serdar Guzel, Baran Gursoy, Gazi Erkan Bostanci
Date: 2022-03-09 00:00:00
Tasks: Object, object-detection, Object Detection, Small Object Detection

Small object detection has major applications in the fields of UAVs, surveillance, farming and many others. In this work we investigate the performance of state of the art Yolo based object detection models for the task of small object detection as they are one of the most popular and easy to use object detection models. We evaluated YOLOv5 and YOLOX models in this study. We also investigate the effects of slicing aided inference and fine-tuning the model for slicing aided inference. We used the VisDrone2019Det dataset for training and evaluating our models. This dataset is challenging in the sense that most objects are relatively small compared to the image sizes. This work aims to benchmark the YOLOv5 and YOLOX models for small object detection. We have seen that sliced inference increases the AP50 score in all experiments, this effect was greater for the YOLOv5 models compared to the YOLOX models. The effects of sliced fine-tuning and sliced inference combined produced substantial improvement for all models. The highest AP50 score was achieved by the YOLOv5- Large model on the VisDrone2019Det test-dev subset with the score being 48.8.
Multiscale Convolutional Transformer with Center Mask Pretraining for Hyperspectral Image Classification
Authors: Sen Jia, Yifan Wang
Date: 2022-03-09 00:00:00
Tasks: Classification, Decoder, Hyperspectral Image Classification, image-classification, Image Classification

Hyperspectral images (HSI) not only have a broad macroscopic field of view but also contain rich spectral information, and the types of surface objects can be identified through spectral information, which is one of the main applications in hyperspectral image related research.In recent years, more and more deep learning methods have been proposed, among which convolutional neural networks (CNN) are the most influential. However, CNN-based methods are difficult to capture long-range dependencies, and also require a large amount of labeled data for model training.Besides, most of the self-supervised training methods in the field of HSI classification are based on the reconstruction of input samples, and it is difficult to achieve effective use of unlabeled samples. To address the shortcomings of CNN networks, we propose a noval multi-scale convolutional embedding module for HSI to realize effective extraction of spatial-spectral information, which can be better combined with Transformer network.In order to make more efficient use of unlabeled data, we propose a new self-supervised pretask. Similar to Mask autoencoder, but our pre-training method only masks the corresponding token of the central pixel in the encoder, and inputs the remaining token into the decoder to reconstruct the spectral information of the central pixel.Such a pretask can better model the relationship between the central feature and the domain feature, and obtain more stable training results.
Human Gaze Guided Attention for Surgical Activity Recognition
Authors: Abdishakour Awale, Duygu Sarikaya
Date: 2022-03-09 00:00:00
Tasks: Activity Recognition, Video Understanding

Modeling and automatically recognizing surgical activities are fundamental steps toward automation in surgery and play important roles in providing timely feedback to surgeons. Accurately recognizing surgical activities in video poses a challenging problem that requires an effective means of learning both spatial and temporal dynamics. Human gaze and visual saliency carry important information about visual attention and can be used to extract more relevant features that better reflect these spatial and temporal dynamics. In this study, we propose to use human gaze with a spatio-temporal attention mechanism for activity recognition in surgical videos. Our model consists of an I3D-based architecture, learns spatio-temporal features using 3D convolutions, as well as learns an attention map using human gaze as supervision. We evaluate our model on the Suturing task of JIGSAWS which is a publicly available surgical video understanding dataset. To our knowledge, we are the first to use human gaze for surgical activity recognition. Our results and ablation studies support the contribution of using human gaze to guide attention by outperforming state-of-the art models with an accuracy of 85.4%.
Defending Black-box Skeleton-based Human Activity Classifiers
Authors: He Wang, Yunfeng Diao, Zichang Tan, Guodong Guo
Date: 2022-03-09 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series Analysis

Skeletal motions have been heavily replied upon for human activity recognition (HAR). Recently, a universal vulnerability of skeleton-based HAR has been identified across a variety of classifiers and data, calling for mitigation. To this end, we propose the first black-box defense method for skeleton-based HAR to our best knowledge. Our method is featured by full Bayesian treatments of the clean data, the adversaries and the classifier, leading to (1) a new Bayesian Energy-based formulation of robust discriminative classifiers, (2) a new adversary sampling scheme based on natural motion manifolds, and (3) a new post-train Bayesian strategy for black-box defense. We name our framework Bayesian Energy-based Adversarial Training or BEAT. BEAT is straightforward but elegant, which turns vulnerable black-box classifiers into robust ones without sacrificing accuracy. It demonstrates surprising and universal effectiveness across a wide range of skeletal HAR classifiers and datasets, under various attacks. Code is available at https://github.com/realcrane/RobustActionRecogniser.