pwc-archive/papers-with-abstracts huggingface dataset:
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking
Authors: Shuai Jia, Yibing Song, Chao Ma, Xiaokang Yang
Date: 2021-03-27 00:00:00
Proceeding: CVPR 2021 1
Tasks: Adversarial Attack, image-classification, Image Classification, Object, Object Tracking, Visual Object Tracking, Visual Tracking

Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers. Assuming that the model structures of deep trackers are known, a variety of white-box attack approaches to visual tracking have demonstrated promising results. However, the model knowledge about deep trackers is usually unavailable in real applications. In this paper, we propose a decision-based black-box attack method for visual object tracking. In contrast to existing black-box adversarial attack methods that deal with static images for image classification, we propose IoU attack that sequentially generates perturbations based on the predicted IoU scores from both current and historical frames. By decreasing the IoU scores, the proposed attack method degrades the accuracy of temporal coherent bounding boxes (i.e., object motions) accordingly. In addition, we transfer the learned perturbations to the next few frames to initialize temporal motion attack. We validate the proposed IoU attack on state-of-the-art deep trackers (i.e., detection based, correlation filter based, and long-term trackers). Extensive experiments on the benchmark datasets indicate the effectiveness of the proposed IoU attack method. The source code is available at https://github.com/VISION-SJTU/IoUattack.
Community-based Cyberreading for Information Understanding
Authors: Zhuoren Jiang, Xiaozhong Liu, Liangcai Gao, Zhi Tang
Date: 2021-03-27 00:00:00
Tasks: Learning-To-Rank

Although the content in scientific publications is increasingly challenging, it is necessary to investigate another important problem, that of scientific information understanding. For this proposed problem, we investigate novel methods to assist scholars (readers) to better understand scientific publications by enabling physical and virtual collaboration. For physical collaboration, an algorithm will group readers together based on their profiles and reading behavior, and will enable the cyberreading collaboration within a online reading group. For virtual collaboration, instead of pushing readers to communicate with others, we cluster readers based on their estimated information needs. For each cluster, a learning to rank model will be generated to recommend readers' communitized resources (i.e., videos, slides, and wikis) to help them understand the target publication.
Hyperbolic Geometry is Not Necessary: Lightweight Euclidean-Based Models for Low-Dimensional Knowledge Graph Embeddings
Authors: Kai Wang, Yu Liu, Dan Lin, Quan Z. Sheng
Date: 2021-03-27 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Graph Embedding, Knowledge Graph Embedding, Knowledge Graph Embeddings, Link Prediction

Recent knowledge graph embedding (KGE) models based on hyperbolic geometry have shown great potential in a low-dimensional embedding space. However, the necessity of hyperbolic space in KGE is still questionable, because the calculation based on hyperbolic geometry is much more complicated than Euclidean operations. In this paper, based on the state-of-the-art hyperbolic-based model RotH, we develop two lightweight Euclidean-based models, called RotL and Rot2L. The RotL model simplifies the hyperbolic operations while keeping the flexible normalization effect. Utilizing a novel two-layer stacked transformation and based on RotL, the Rot2L model obtains an improved representation capability, yet costs fewer parameters and calculations than RotH. The experiments on link prediction show that Rot2L achieves the state-of-the-art performance on two widely-used datasets in low-dimensional knowledge graph embeddings. Furthermore, RotL achieves similar performance as RotH but only requires half of the training time.
You Can Do Better! If You Elaborate the Reason When Making Prediction
Authors: Dongfang Li, Jingcong Tao, Qingcai Chen, Baotian Hu
Date: 2021-03-27 00:00:00
Tasks: Multiple-choice, Natural Language Inference, Question Answering

Neural predictive models have achieved remarkable performance improvements in various natural language processing tasks. However, most neural predictive models suffer from the lack of explainability of predictions, limiting their practical utility. This paper proposes a neural predictive approach to make a prediction and generate its corresponding explanation simultaneously. It leverages the knowledge entailed in explanations as an additional distillation signal for more efficient learning. We conduct a preliminary study on Chinese medical multiple-choice question answering, English natural language inference, and commonsense question answering tasks. The experimental results show that the proposed approach can generate reasonable explanations for its predictions even with a small-scale training corpus. The proposed method also achieves improved prediction accuracy on three datasets, which indicates that making predictions can benefit from generating the explanation in the decision process.
Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection
Authors: Stefano Menini, Alessio Palmero Aprosio, Sara Tonelli
Date: 2021-03-27 00:00:00
Tasks: Abusive Language, General Classification

The datasets most widely used for abusive language detection contain lists of messages, usually tweets, that have been manually judged as abusive or not by one or more annotators, with the annotation performed at message level. In this paper, we investigate what happens when the hateful content of a message is judged also based on the context, given that messages are often ambiguous and need to be interpreted in the context of occurrence. We first re-annotate part of a widely used dataset for abusive language detection in English in two conditions, i.e. with and without context. Then, we compare the performance of three classification algorithms obtained on these two types of dataset, arguing that a context-aware classification is more challenging but also more similar to a real application scenario.
MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis
Authors: Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, Gim Hee Lee
Date: 2021-03-27 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction, Depth Estimation, NeRF, Novel View Synthesis

In this paper, we propose MINE to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image. Our approach is a continuous depth generalization of the Multiplane Images (MPI) by introducing the NEural radiance fields (NeRF). Given a single image as input, MINE predicts a 4-channel image (RGB and volume density) at arbitrary depth values to jointly reconstruct the camera frustum and fill in occluded contents. The reconstructed and inpainted frustum can then be easily rendered into novel RGB or depth views using differentiable rendering. Extensive experiments on RealEstate10K, KITTI and Flowers Light Fields show that our MINE outperforms state-of-the-art by a large margin in novel view synthesis. We also achieve competitive results in depth estimation on iBims-1 and NYU-v2 without annotated depth supervision. Our source code is available at https://github.com/vincentfung13/MINE
Embedding Transfer with Label Relaxation for Improved Metric Learning
Authors: Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak
Date: 2021-03-27 00:00:00
Proceeding: CVPR 2021 1
Tasks: Knowledge Distillation, Metric Learning

This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities between samples in the source embedding space as the knowledge, and transfers them through a loss used for learning target embedding models. To this end, we design a new loss called relaxed contrastive loss, which employs the pairwise similarities as relaxed labels for inter-sample relations. Our loss provides a rich supervisory signal beyond class equivalence, enables more important pairs to contribute more to training, and imposes no restriction on manifolds of target embedding spaces. Experiments on metric learning benchmarks demonstrate that our method largely improves performance, or reduces sizes and output dimensions of target models effectively. We further show that it can be also used to enhance quality of self-supervised representation and performance of classification models. In all the experiments, our method clearly outperforms existing embedding transfer techniques.
Frequency-specific segregation and integration of human cerebral cortex: an intrinsic functional atlas
Authors: Zhiguo Luo, Ling-Li Zeng, Hui Shen, Dewen Hu
Date: 2021-03-27 00:00:00
Tasks: Clustering, Functional Connectivity

The frequency-specific coupling mechanism of the functional human brain networks underpins its complex cognitive and behavioral functions. Nevertheless, it is not well unveiled what are the frequency-specific subdivisions and network topologies of the human brain. In this study, we estimated functional connectivity of the human cerebral cortex using spectral connection, and conducted frequency-specific parcellation using eigen-clustering and gradient-based methods, and then explored their topological structures. 7T fMRI data of 184 subjects in the HCP dataset were used for parcellation and exploring the topological properties of the functional networks, and 3T fMRI data of another 890 subjects were used to confirm the stability of the frequency-specific topologies. Seven to ten functional networks were stably integrated by two to four dissociable hub categories at specific frequencies, and we proposed an intrinsic functional atlas containing 456 parcels according to the parcellations across frequencies. The results revealed that the functional networks contained stable frequency-specific topologies, which may imply more abundant roles of the functional units and more complex interactions among them.
SceneGraphFusion: Incremental 3D Scene Graph Prediction from RGB-D Sequences
Authors: Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, Federico Tombari
Date: 2021-03-27 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Object Classification, 3d scene graph generation, Graph Neural Network, Panoptic Segmentation, Predicate Classification, Scene Graph Generation, Scene Understanding

Scene graphs are a compact and explicit representation successfully used in a variety of 2D scene understanding tasks. This work proposes a method to incrementally build up semantic scene graphs from a 3D environment given a sequence of RGB-D frames. To this end, we aggregate PointNet features from primitive scene components by means of a graph neural network. We also propose a novel attention mechanism well suited for partial and missing graph data present in such an incremental reconstruction scenario. Although our proposed method is designed to run on submaps of the scene, we show it also transfers to entire 3D scenes. Experiments show that our approach outperforms 3D scene graph prediction methods by a large margin and its accuracy is on par with other 3D semantic and panoptic segmentation methods while running at 35 Hz.
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey
Authors: Ningbo Zhu, Fei Yang
Date: 2021-03-27 00:00:00
Tasks: Denoising, Survey

After an artificial model background subtraction, the pixels have been labelled as foreground and background. Previous approaches to secondary processing the output for denoising usually use traditional methods such as the Bayesian refinement method. In this paper, we focus on using a modified U-Net model to approximate the result of the Bayesian refinement method and improve the result. In our modified U-Net model, the result of background subtraction from other models will be combined with the source image as input for learning the statistical distribution. Thus, the losing information caused by the background subtraction model can be restored from the source image. Moreover, since the part of the input image is already the output of the other background subtraction model, the feature extraction should be convenient, it only needs to change the labels of the noise pixels. Compare with traditional methods, using deep learning methods superiority in keeping details.
Feature-based Representation for Violin Bridge Admittances
Authors: R. Malvermi, S. Gonzalez, M. Quintavalla, F. Antonacci, A. Sarti, J. A. Torres, R. Corradi
Date: 2021-03-27 00:00:00

Frequency Response Functions (FRFs) are one of the cornerstones of musical acoustic experimental research. They describe the way in which musical instruments vibrate in a wide range of frequencies and are used to predict and understand the acoustic differences between them. In the specific case of stringed musical instruments such as violins, FRFs evaluated at the bridge are known to capture the overall body vibration. These indicators, also called bridge admittances, are widely used in the literature for comparative analyses. However, due to their complex structure they are rather difficult to quantitatively compare and study. In this manuscript we present a way to quantify differences between FRFs, in particular violin bridge admittances, that separates the effects in frequency, amplitude and quality factor of the first resonance peaks characterizing the responses. This approach allows us to define a distance between FRFs and clusterise measurements according to this distance. We use two case studies, one based on Finite Element Analysis and another exploiting measurements on real violins, to prove the effectiveness of such representation. In particular, for simulated bridge admittances the proposed distance is able to highlight the different impact of consecutive simulation `steps' on specific vibrational properties and, for real violins, gives a first insight on similar styles of making, as well as opposite ones.
Self-adaptive Torque Vectoring Controller Using Reinforcement Learning
Authors: Shayan Taherian, Sampo Kuutti, Marco Visca, Saber Fallah
Date: 2021-03-27 00:00:00
Tasks: Friction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Continuous direct yaw moment control systems such as torque-vectoring controller are an essential part for vehicle stabilization. This controller has been extensively researched with the central objective of maintaining the vehicle stability by providing consistent stable cornering response. The ability of careful tuning of the parameters in a torque-vectoring controller can significantly enhance vehicle's performance and stability. However, without any re-tuning of the parameters, especially in extreme driving conditions e.g. low friction surface or high velocity, the vehicle fails to maintain the stability. In this paper, the utility of Reinforcement Learning (RL) based on Deep Deterministic Policy Gradient (DDPG) as a parameter tuning algorithm for torque-vectoring controller is presented. It is shown that, torque-vectoring controller with parameter tuning via reinforcement learning performs well on a range of different driving environment e.g., wide range of friction conditions and different velocities, which highlight the advantages of reinforcement learning as an adaptive algorithm for parameter tuning. Moreover, the robustness of DDPG algorithm are validated under scenarios which are beyond the training environment of the reinforcement learning algorithm. The simulation has been carried out using a four wheels vehicle model with nonlinear tire characteristics. We compare our DDPG based parameter tuning against a genetic algorithm and a conventional trial-and-error tunning of the torque vectoring controller, and the results demonstrated that the reinforcement learning based parameter tuning significantly improves the stability of the vehicle.
KnowRU: Knowledge Reusing via Knowledge Distillation in Multi-agent Reinforcement Learning
Authors: Zijian Gao, Kele Xu, Bo Ding, Huaimin Wang, Yiying Li, Hongda Jia
Date: 2021-03-27 00:00:00
Tasks: Deep Reinforcement Learning, Knowledge Distillation, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recently, deep Reinforcement Learning (RL) algorithms have achieved dramatically progress in the multi-agent area. However, training the increasingly complex tasks would be time-consuming and resources-exhausting. To alleviate this problem, efficient leveraging the historical experience is essential, which is under-explored in previous studies as most of the exiting methods may fail to achieve this goal in a continuously variational system due to their complicated design and environmental dynamics. In this paper, we propose a method, named "KnowRU" for knowledge reusing which can be easily deployed in the majority of the multi-agent reinforcement learning algorithms without complicated hand-coded design. We employ the knowledge distillation paradigm to transfer the knowledge among agents with the goal to accelerate the training phase for new tasks, while improving the asymptotic performance of agents. To empirically demonstrate the robustness and effectiveness of KnowRU, we perform extensive experiments on state-of-the-art multi-agent reinforcement learning (MARL) algorithms on collaborative and competitive scenarios. The results show that KnowRU can outperform the recently reported methods, which emphasizes the importance of the proposed knowledge reusing for MARL.
An Efficiently Coupled Shape and Appearance Prior for Active Contour Segmentation
Authors: Martin Mueller, Navdeep Dahiya, Anthony Yezzi
Date: 2021-03-27 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper proposes a novel training model based on shape and appearance features for object segmentation in images and videos. Whereas most such models rely on two-dimensional appearance templates or a finite set of descriptors, our appearance-based feature is a one-dimensional function, which is efficiently coupled with the object's shape by integrating intensities along the object's iso-contours. Joint PCA training on these shape and appearance features further exploits shape-appearance correlations and the resulting training model is incorporated in an active-contour-type energy functional for recognition-segmentation tasks. Experiments on synthetic and infrared images demonstrate how this shape and appearance training model improves accuracy compared to methods based on the Chan-Vese energy.
Generalization over different cellular automata rules learned by a deep feed-forward neural network
Authors: Marcel Aach, Jens Henrik Goebbert, Jenia Jitsev
Date: 2021-03-27 00:00:00
Tasks: Decoder

To test generalization ability of a class of deep neural networks, we randomly generate a large number of different rule sets for 2-D cellular automata (CA), based on John Conway's Game of Life. Using these rules, we compute several trajectories for each CA instance. A deep convolutional encoder-decoder network with short and long range skip connections is trained on various generated CA trajectories to predict the next CA state given its previous states. Results show that the network is able to learn the rules of various, complex cellular automata and generalize to unseen configurations. To some extent, the network shows generalization to rule sets and neighborhood sizes that were not seen during the training at all. Code to reproduce the experiments is publicly available at: https://github.com/SLAMPAI/generalization-cellular-automata
COVID-19 personal protective equipment detection using real-time deep learning methods
Authors: Shayan Khosravipour, Erfan Taghvaei, Nasrollah Moghadam Charkari
Date: 2021-03-27 00:00:00
Tasks: object-detection, Object Detection

The exponential spread of COVID-19 in over 215 countries has led WHO to recommend face masks and gloves for a safe return to school or work. We used artificial intelligence and deep learning algorithms for automatic face masks and gloves detection in public areas. We investigated and assessed the efficacy of two popular deep learning algorithms of YOLO (You Only Look Once) and SSD MobileNet for the detection and proper wearing of face masks and gloves trained over a data set of 8250 images imported from the internet. YOLOv3 is implemented using the DarkNet framework, and the SSD MobileNet algorithm is applied for the development of accurate object detection. The proposed models have been developed to provide accurate multi-class detection (Mask vs. No-Mask vs. Gloves vs. No-Gloves vs. Improper). When people wear their masks improperly, the method detects them as an improper class. The introduced models provide accuracies of (90.6% for YOLO and 85.5% for SSD) for multi-class detection. The systems' results indicate the efficiency and validity of detecting people who do not wear masks and gloves in public.
Few-shot Semantic Image Synthesis Using StyleGAN Prior
Authors: Yuki Endo, Yoshihiro Kanamori
Date: 2021-03-27 00:00:00
Tasks: Image Generation

This paper tackles a challenging problem of generating photorealistic images from semantic layouts in few-shot scenarios where annotated training pairs are hardly available but pixel-wise annotation is quite costly. We present a training strategy that performs pseudo labeling of semantic masks using the StyleGAN prior. Our key idea is to construct a simple mapping between the StyleGAN feature and each semantic class from a few examples of semantic masks. With such mappings, we can generate an unlimited number of pseudo semantic masks from random noise to train an encoder for controlling a pre-trained StyleGAN generator. Although the pseudo semantic masks might be too coarse for previous approaches that require pixel-aligned masks, our framework can synthesize high-quality images from not only dense semantic masks but also sparse inputs such as landmarks and scribbles. Qualitative and quantitative results with various datasets demonstrate improvement over previous approaches with respect to layout fidelity and visual quality in as few as one- or five-shot settings.
Human-in-the-loop Handling of Knowledge Drift
Authors: Andrea Bontempelli, Fausto Giunchiglia, Andrea Passerini, Stefano Teso
Date: 2021-03-27 00:00:00
Tasks: Drift Detection

We introduce and study knowledge drift (KD), a complex form of drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations between them can all change over time. The main challenge is that, since the ground-truth concept hierarchy is unobserved, it is hard to tell apart different forms of KD. For instance, introducing a new is-a relation between two concepts might be confused with individual changes to those concepts, but it is far from equivalent. Failure to identify the right kind of KD compromises the concept hierarchy used by the classifier, leading to systematic prediction errors. Our key observation is that in many human-in-the-loop applications (like smart personal assistants) the user knows whether and what kind of drift occurred recently. Motivated by this, we introduce TRCKD, a novel approach that combines automated drift detection and adaptation with an interactive stage in which the user is asked to disambiguate between different kinds of KD. In addition, TRCKD implements a simple but effective knowledge-aware adaptation strategy. Our simulations show that often a handful of queries to the user are enough to substantially improve prediction performance on both synthetic and realistic data.