pwc-archive/papers-with-abstracts huggingface dataset:
Generalizing Face Forgery Detection with High-frequency Features
Authors: Yuchen Luo, Yong Zhang, Junchi Yan, Wei Liu
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Vocal Bursts Intensity Prediction

Current face forgery detection methods achieve high accuracy under the within-database scenario where training and testing forgeries are synthesized by the same algorithm. However, few of them gain satisfying performance under the cross-database scenario where training and testing forgeries are synthesized by different algorithms. In this paper, we find that current CNN-based detectors tend to overfit to method-specific color textures and thus fail to generalize. Observing that image noises remove color textures and expose discrepancies between authentic and tampered regions, we propose to utilize the high-frequency noises for face forgery detection. We carefully devise three functional modules to take full advantage of the high-frequency features. The first is the multi-scale high-frequency feature extraction module that extracts high-frequency noises at multiple scales and composes a novel modality. The second is the residual-guided spatial attention module that guides the low-level RGB feature extractor to concentrate more on forgery traces from a new perspective. The last is the cross-modality attention module that leverages the correlation between the two complementary modalities to promote feature learning for each other. Comprehensive evaluations on several benchmark databases corroborate the superior generalization performance of our proposed method.
Unsupervised domain adaptation via coarse-to-fine feature alignment method using contrastive learning
Authors: Shiyu Tang, Peijun Tang, Yanxiang Gong, Zheng Ma, Mei Xie
Date: 2021-03-23 00:00:00
Tasks: Contrastive Learning, Domain Adaptation, Semantic Segmentation, Unsupervised Domain Adaptation

Previous feature alignment methods in Unsupervised domain adaptation(UDA) mostly only align global features without considering the mismatch between class-wise features. In this work, we propose a new coarse-to-fine feature alignment method using contrastive learning called CFContra. It draws class-wise features closer than coarse feature alignment or class-wise feature alignment only, therefore improves the model's performance to a great extent. We build it upon one of the most effective methods of UDA called entropy minimization to further improve performance. In particular, to prevent excessive memory occupation when applying contrastive loss in semantic segmentation, we devise a new way to build and update the memory bank. In this way, we make the algorithm more efficient and viable with limited memory. Extensive experiments show the effectiveness of our method and model trained on the GTA5 to Cityscapes dataset has boost mIOU by 3.5 compared to the MinEnt algorithm. Our code will be publicly available.
Multi-Robot Task Allocation -- Complexity and Approximation
Authors: Haris Aziz, Hau Chan, Ágnes Cseh, Bo Li, Fahimeh Ramezani, Chenhao Wang
Date: 2021-03-23 00:00:00

Multi-robot task allocation is one of the most fundamental classes of problems in robotics and is crucial for various real-world robotic applications such as search, rescue and area exploration. We consider the Single-Task robots and Multi-Robot tasks Instantaneous Assignment (ST-MR-IA) setting where each task requires at least a certain number of robots and each robot can work on at most one task and incurs an operational cost for each task. Our aim is to consider a natural computational problem of allocating robots to complete the maximum number of tasks subject to budget constraints. We consider budget constraints of three different kinds: (1) total budget, (2) task budget, and (3) robot budget. We provide a detailed complexity analysis including results on approximations as well as polynomial-time algorithms for the general setting and important restricted settings.
ReCU: Reviving the Dead Weights in Binary Neural Networks
Authors: Zihan Xu, Mingbao Lin, Jianzhuang Liu, Jie Chen, Ling Shao, Yue Gao, Yonghong Tian, Rongrong Ji
Date: 2021-03-23 00:00:00
Proceeding: ICCV 2021 10
Tasks: Binarization, Quantization

Binary neural networks (BNNs) have received increasing attention due to their superior reductions of computation and memory. Most existing works focus on either lessening the quantization error by minimizing the gap between the full-precision weights and their binarization or designing a gradient approximation to mitigate the gradient mismatch, while leaving the "dead weights" untouched. This leads to slow convergence when training BNNs. In this paper, for the first time, we explore the influence of "dead weights" which refer to a group of weights that are barely updated during the training of BNNs, and then introduce rectified clamp unit (ReCU) to revive the "dead weights" for updating. We prove that reviving the "dead weights" by ReCU can result in a smaller quantization error. Besides, we also take into account the information entropy of the weights, and then mathematically analyze why the weight standardization can benefit BNNs. We demonstrate the inherent contradiction between minimizing the quantization error and maximizing the information entropy, and then propose an adaptive exponential scheduler to identify the range of the "dead weights". By considering the "dead weights", our method offers not only faster BNN training, but also state-of-the-art performance on CIFAR-10 and ImageNet, compared with recent methods. Code can be available at https://github.com/z-hXu/ReCU.
A New Approach To Text Rating Classification Using Sentiment Analysis
Authors: Thomas Konstantinovsky
Date: 2021-03-23 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis

Typical use cases of sentiment analysis usually revolve around assessing the probability of a text belonging to a certain sentiment and deriving insight concerning it; little work has been done to explore further use cases derived using those probabilities in the context of rating. In this paper, we redefine the sentiment proportion values as building blocks for a triangle structure, allowing us to derive variables for a new formula for classifying text given in the form of product reviews into a group of higher and a group of lower ratings and prove a dependence exists between the sentiments and the ratings.
Group-aware Label Transfer for Domain Adaptive Person Re-identification
Authors: Kecheng Zheng, Wu Liu, Lingxiao He, Tao Mei, Jiebo Luo, Zheng-Jun Zha
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Attribute, Clustering, Domain Adaptive Person Re-Identification, Online Clustering, Person Re-Identification, Pseudo Label, Representation Learning

Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion. However, offline interaction between these two steps may allow noisy pseudo labels to substantially hinder the capability of the model. In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning. Specifically, a label transfer algorithm simultaneously uses pseudo labels to train the data while refining the pseudo labels as an online clustering algorithm. It treats the online label refinery problem as an optimal transport problem, which explores the minimum cost for assigning M samples to N pseudo labels. More importantly, we introduce a group-aware strategy to assign implicit attribute group IDs to samples. The combination of the online label refining algorithm and the group-aware strategy can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity. The effectiveness of the proposed GLT is demonstrated by the experimental results (Rank-1 accuracy) for Market1501$\to$DukeMTMC (82.0\%) and DukeMTMC$\to$Market1501 (92.2\%), remarkably closing the gap between unsupervised and supervised performance on person re-identification.
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition
Authors: Henrique Siqueira, Pablo Barros, Sven Magg, Cornelius Weber, Stefan Wermter
Date: 2021-03-23 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

In domains where computational resources and labeled data are limited, such as in robotics, deep networks with millions of weights might not be the optimal solution. In this paper, we introduce a connectivity scheme for pyramidal architectures to increase their capacity for learning features. Experiments on facial expression recognition of unseen people demonstrate that our approach is a potential candidate for applications with restricted resources, due to good generalization performance and low computational cost. We show that our approach generalizes as well as convolutional architectures in this task but uses fewer trainable parameters and is more robust for low-resolution faces.
Discovering Emotion and Reasoning its Flip in Multi-Party Conversations using Masked Memory Network and Transformer
Authors: Shivani Kumar, Anubhav Shrimal, Md Shad Akhtar, Tanmoy Chakraborty
Date: 2021-03-23 00:00:00
Tasks: Emotion Recognition

Efficient discovery of a speaker's emotional states in a multi-party conversation is significant to design human-like conversational agents. During a conversation, the cognitive state of a speaker often alters due to certain past utterances, which may lead to a flip in their emotional state. Therefore, discovering the reasons (triggers) behind the speaker's emotion-flip during a conversation is essential to explain the emotion labels of individual utterances. In this paper, along with addressing the task of emotion recognition in conversations (ERC), we introduce a novel task - Emotion-Flip Reasoning (EFR), that aims to identify past utterances which have triggered one's emotional state to flip at a certain time. We propose a masked memory network to address the former and a Transformer-based network for the latter task. To this end, we consider MELD, a benchmark emotion recognition dataset in multi-party conversations for the task of ERC, and augment it with new ground-truth labels for EFR. An extensive comparison with five state-of-the-art models suggests improved performances of our models for both tasks. We further present anecdotal evidence and both qualitative and quantitative error analyses to support the superiority of our models compared to the baselines.
iMAP: Implicit Mapping and Positioning in Real-Time
Authors: Edgar Sucar, Shikun Liu, Joseph Ortiz, Andrew J. Davison
Date: 2021-03-23 00:00:00
Proceeding: ICCV 2021 10

We show for the first time that a multilayer perceptron (MLP) can serve as the only scene representation in a real-time SLAM system for a handheld RGB-D camera. Our network is trained in live operation without prior data, building a dense, scene-specific implicit 3D model of occupancy and colour which is also immediately used for tracking. Achieving real-time SLAM via continual training of a neural network against a live image stream requires significant innovation. Our iMAP algorithm uses a keyframe structure and multi-processing computation flow, with dynamic information-guided pixel sampling for speed, with tracking at 10 Hz and global map updating at 2 Hz. The advantages of an implicit MLP over standard dense SLAM techniques include efficient geometry representation with automatic detail control and smooth, plausible filling-in of unobserved regions such as the back surfaces of objects.
Roughness Index and Roughness Distance for Benchmarking Medical Segmentation
Authors: Vidhiwar Singh Rathour, Kashu Yamakazi, T. Hoang Ngan Le
Date: 2021-03-23 00:00:00
Tasks: Benchmarking, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Object, Segmentation, Semantic Segmentation

Medical image segmentation is one of the most challenging tasks in medical image analysis and has been widely developed for many clinical applications. Most of the existing metrics have been first designed for natural images and then extended to medical images. While object surface plays an important role in medical segmentation and quantitative analysis i.e. analyze brain tumor surface, measure gray matter volume, most of the existing metrics are limited when it comes to analyzing the object surface, especially to tell about surface smoothness or roughness of a given volumetric object or to analyze the topological errors. In this paper, we first analysis both pros and cons of all existing medical image segmentation metrics, specially on volumetric data. We then propose an appropriate roughness index and roughness distance for medical image segmentation analysis and evaluation. Our proposed method addresses two kinds of segmentation errors, i.e. (i)topological errors on boundary/surface and (ii)irregularities on the boundary/surface. The contribution of this work is four-fold: (i) detect irregular spikes/holes on a surface, (ii) propose roughness index to measure surface roughness of a given object, (iii) propose a roughness distance to measure the distance of two boundaries/surfaces by utilizing the proposed roughness index and (iv) suggest an algorithm which helps to remove the irregular spikes/holes to smooth the surface. Our proposed roughness index and roughness distance are built upon the solid surface roughness parameter which has been successfully developed in the civil engineering.
Shared Latent Space of Font Shapes and Their Noisy Impressions
Authors: Jihun Kang, Daichi Haraguchi, Seiya Matsuda, Akisato Kimura, Seiichi Uchida
Date: 2021-03-23 00:00:00

Styles of typefaces or fonts are often associated with specific impressions, such as heavy, contemporary, or elegant. This indicates that there are certain correlations between font shapes and their impressions. To understand the correlations, this paper realizes a shared latent space where a font and its impressions are embedded nearby. The difficulty is that the impression words attached to a font are often very noisy. This is because impression words are very subjective and diverse. More importantly, some impression words have no direct relevance to the font shapes and will disturb the realization of the shared latent space. We, therefore, use DeepSets for enhancing shape-relevant words and suppressing shape irrelevant words automatically while training the shared latent space. Quantitative and qualitative experimental results with a large-scale font-impression dataset demonstrate that the shared latent space by the proposed method describes the correlation appropriately, especially for the shape-relevant impression words.
Co-Grounding Networks with Semantic Attention for Referring Expression Comprehension in Videos
Authors: Sijie Song, Xudong Lin, Jiaying Liu, Zongming Guo, Shih-Fu Chang
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Referring Expression, Referring Expression Comprehension, Video Grounding

In this paper, we address the problem of referring expression comprehension in videos, which is challenging due to complex expression and scene dynamics. Unlike previous methods which solve the problem in multiple stages (i.e., tracking, proposal-based matching), we tackle the problem from a novel perspective, \textbf{co-grounding}, with an elegant one-stage framework. We enhance the single-frame grounding accuracy by semantic attention learning and improve the cross-frame grounding consistency with co-grounding feature learning. Semantic attention learning explicitly parses referring cues in different attributes to reduce the ambiguity in the complex expression. Co-grounding feature learning boosts visual feature representations by integrating temporal correlation to reduce the ambiguity caused by scene dynamics. Experiment results demonstrate the superiority of our framework on the video grounding datasets VID and LiOTB in generating accurate and stable results across frames. Our model is also applicable to referring expression comprehension in images, illustrated by the improved performance on the RefCOCO dataset. Our project is available at https://sijiesong.github.io/co-grounding.
The Success of AdaBoost and Its Application in Portfolio Management
Authors: Yijian Chuan, Chaoyi Zhao, Zhenrui He, Lan Wu
Date: 2021-03-23 00:00:00
Tasks: Binary Classification, Management

We develop a novel approach to explain why AdaBoost is a successful classifier. By introducing a measure of the influence of the noise points (ION) in the training data for the binary classification problem, we prove that there is a strong connection between the ION and the test error. We further identify that the ION of AdaBoost decreases as the iteration number or the complexity of the base learners increases. We confirm that it is impossible to obtain a consistent classifier without deep trees as the base learners of AdaBoost in some complicated situations. We apply AdaBoost in portfolio management via empirical studies in the Chinese market, which corroborates our theoretical propositions.
Joint Distribution across Representation Space for Out-of-Distribution Detection
Authors: Jingwei Xu, Siyuan Zhu, Zenan Li, Chang Xu
Date: 2021-03-23 00:00:00
Tasks: Out-of-Distribution Detection, Out of Distribution (OOD) Detection

Deep neural networks (DNNs) have become a key part of many modern software applications. After training and validating, the DNN is deployed as an irrevocable component and applied in real-world scenarios. Although most DNNs are built meticulously with huge volumes of training data, data in the real world still remain unknown to the DNN model, which leads to the crucial requirement of runtime out-of-distribution (OOD) detection. However, many existing approaches 1) need OOD data for classifier training or parameter tuning, or 2) simply combine the scores of each hidden layer as an ensemble of features for OOD detection. In this paper, we present a novel outlook on in-distribution data in a generative manner, which takes their latent features generated from each hidden layer as a joint distribution across representation spaces. Since only the in-distribution latent features are comprehensively understood in representation space, the internal difference between in-distribution and OOD data can be naturally revealed without the intervention of any OOD data. Specifically, We construct a generative model, called Latent Sequential Gaussian Mixture (LSGM), to depict how the in-distribution latent features are generated in terms of the trace of DNN inference across representation spaces. We first construct the Gaussian Mixture Model (GMM) based on in-distribution latent features for each hidden layer, and then connect GMMs via the transition probabilities of the inference traces. Experimental evaluations on popular benchmark OOD datasets and models validate the superiority of the proposed method over the state-of-the-art methods in OOD detection.
Deep Occlusion-Aware Instance Segmentation with Overlapping BiLayers
Authors: Lei Ke, Yu-Wing Tai, Chi-Keung Tang
Date: 2021-03-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Amodal Instance Segmentation, Boundary Detection, Instance Segmentation, Object Detection, Occlusion Handling, Real-time Instance Segmentation, Semantic Segmentation

Segmenting highly-overlapping objects is challenging, because typically no distinction is made between real object contours and occlusion boundaries. Unlike previous two-stage instance segmentation methods, we model image formation as composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top GCN layer detects the occluding objects (occluder) and the bottom GCN layer infers partially occluded instance (occludee). The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression. We validate the efficacy of bilayer decoupling on both one-stage and two-stage object detectors with different backbones and network layer choices. Despite its simplicity, extensive experiments on COCO and KINS show that our occlusion-aware BCNet achieves large and consistent performance gain especially for heavy occlusion cases. Code is available at https://github.com/lkeab/BCNet.
Salient Image Matting
Authors: Rahul Deora, Rishab Sharma, Dinesh Samuel Sathia Raj
Date: 2021-03-23 00:00:00
Tasks: Image Matting, Object, object-detection, Object Detection, Salient Object Detection

In this paper, we propose an image matting framework called Salient Image Matting to estimate the per-pixel opacity value of the most salient foreground in an image. To deal with a large amount of semantic diversity in images, a trimap is conventionally required as it provides important guidance about object semantics to the matting process. However, creating a good trimap is often expensive and timeconsuming. The SIM framework simultaneously deals with the challenge of learning a wide range of semantics and salient object types in a fully automatic and an end to end manner. Specifically, our framework is able to produce accurate alpha mattes for a wide range of foreground objects and cases where the foreground class, such as human, appears in a very different context than the train data directly from an RGB input. This is done by employing a salient object detection model to produce a trimap of the most salient object in the image in order to guide the matting model about higher-level object semantics. Our framework leverages large amounts of coarse annotations coupled with a heuristic trimap generation scheme to train the trimap prediction network so it can produce trimaps for arbitrary foregrounds. Moreover, we introduce a multi-scale fusion architecture for the task of matting to better capture finer, low-level opacity semantics. With high-level guidance provided by the trimap network, our framework requires only a fraction of expensive matting data as compared to other automatic methods while being able to produce alpha mattes for a diverse range of inputs. We demonstrate our framework on a range of diverse images and experimental results show our framework compares favourably against state of art matting methods without the need for a trimap
Contrastive Reasoning in Neural Networks
Authors: Mohit Prabhushankar, Ghassan AlRegib
Date: 2021-03-23 00:00:00
Tasks: Object Recognition

Neural networks represent data as projections on trained weights in a high dimensional manifold. The trained weights act as a knowledge base consisting of causal class dependencies. Inference built on features that identify these dependencies is termed as feed-forward inference. Such inference mechanisms are justified based on classical cause-to-effect inductive reasoning models. Inductive reasoning based feed-forward inference is widely used due to its mathematical simplicity and operational ease. Nevertheless, feed-forward models do not generalize well to untrained situations. To alleviate this generalization challenge, we propose using an effect-to-cause inference model that reasons abductively. Here, the features represent the change from existing weight dependencies given a certain effect. We term this change as contrast and the ensuing reasoning mechanism as contrastive reasoning. In this paper, we formalize the structure of contrastive reasoning and propose a methodology to extract a neural network's notion of contrast. We demonstrate the value of contrastive reasoning in two stages of a neural network's reasoning pipeline : in inferring and visually explaining decisions for the application of object recognition. We illustrate the value of contrastively recognizing images under distortions by reporting an improvement of 3.47%, 2.56%, and 5.48% in average accuracy under the proposed contrastive framework on CIFAR-10C, noisy STL-10, and VisDA datasets respectively.