pwc-archive/papers-with-abstracts huggingface dataset:
Full Transformer Framework for Robust Point Cloud Registration with Deep Information Interaction
Authors: Guangyan Chen, Meiling Wang, Yufeng Yue, Qingxiang Zhang, Li Yuan
Date: 2021-12-17 00:00:00
Tasks: Geometric Matching, Point Cloud Registration

Recent Transformer-based methods have achieved advanced performance in point cloud registration by utilizing advantages of the Transformer in order-invariance and modeling dependency to aggregate information. However, they still suffer from indistinct feature extraction, sensitivity to noise, and outliers. The reasons are: (1) the adoption of CNNs fails to model global relations due to their local receptive fields, resulting in extracted features susceptible to noise; (2) the shallow-wide architecture of Transformers and lack of positional encoding lead to indistinct feature extraction due to inefficient information interaction; (3) the omission of geometrical compatibility leads to inaccurate classification between inliers and outliers. To address above limitations, a novel full Transformer network for point cloud registration is proposed, named the Deep Interaction Transformer (DIT), which incorporates: (1) a Point Cloud Structure Extractor (PSE) to model global relations and retrieve structural information with Transformer encoders; (2) a deep-narrow Point Feature Transformer (PFT) to facilitate deep information interaction across two point clouds with positional encoding, such that Transformers can establish comprehensive associations and directly learn relative position between points; (3) a Geometric Matching-based Correspondence Confidence Evaluation (GMCCE) method to measure spatial consistency and estimate inlier confidence by designing the triangulated descriptor. Extensive experiments on clean, noisy, partially overlapping point cloud registration demonstrate that our method outperforms state-of-the-art methods.
Distillation of Human-Object Interaction Contexts for Action Recognition
Authors: Muna Almushyti, Frederick W. Li
Date: 2021-12-17 00:00:00
Tasks: Action Recognition, Graph Attention, Human-Object Interaction Detection, Knowledge Distillation, Object, Scene Understanding

Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard informative fine-grained features, which can be captured by learning human-object relationships and interactions. In this paper, we learn human-object relationships by exploiting the interaction of their local and global contexts. We hence propose the Global-Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time via knowledge distillation for fine-grained scene understanding. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network. The local context graphs learn the relation between humans and objects at a frame level by capturing their co-occurrence at a specific time step. The global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. More importantly, we investigate how knowledge from these graphs can be distilled to their counterparts for improving human-object interaction (HOI) recognition. We evaluate our model by conducting comprehensive experiments on two datasets including Charades and CAD-120 datasets. We have achieved better results than the baselines and counterpart approaches.
WebGPT: Browser-assisted question-answering with human feedback
Authors: Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
Date: 2021-12-17 00:00:00
Tasks: Imitation Learning, Navigate, Question Answering

We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.
Dilemma of the Artificial Intelligence Regulatory Landscape
Authors: Weiyue Wu, Shaoshan Liu
Date: 2021-12-17 00:00:00
Tasks: Autonomous Driving

As a startup company in the autonomous driving space, we have undergone four years of painful experiences dealing with a broad spectrum of regulatory requirements. Compared to the software industry norm, which spends 13% of their overall budget on compliances, we were forced to spend 42% of our budget on compliances. Our situation is not alone and, in a way, reflects the dilemma of the artificial intelligence (AI) regulatory landscape. The root cause is the lack of AI expertise in the legislative and executive branches, leading to a lack of standardization for the industry to follow. In this article, we share our first-hand experiences and advocate for the establishment of an FDA-like agency to regulate AI properly.
Optimal discharge of patients from intensive care via a data-driven policy learning framework
Authors: Fernando Lejarza, Jacob Calvert, Misty M Attwood, Daniel Evans, Qingqing Mao
Date: 2021-12-17 00:00:00
Tasks: Management, Off-policy evaluation

Clinical decision support tools rooted in machine learning and optimization can provide significant value to healthcare providers, including through better management of intensive care units. In particular, it is important that the patient discharge task addresses the nuanced trade-off between decreasing a patient's length of stay (and associated hospitalization costs) and the risk of readmission or even death following the discharge decision. This work introduces an end-to-end general framework for capturing this trade-off to recommend optimal discharge timing decisions given a patient's electronic health records. A data-driven approach is used to derive a parsimonious, discrete state space representation that captures a patient's physiological condition. Based on this model and a given cost function, an infinite-horizon discounted Markov decision process is formulated and solved numerically to compute an optimal discharge policy, whose value is assessed using off-policy evaluation strategies. Extensive numerical experiments are performed to validate the proposed framework using real-life intensive care unit patient data.
Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages
Authors: Thomas Mandl, Sandip Modha, Gautam Kishore Shahi, Hiren Madhu, Shrey Satapara, Prasenjit Majumder, Johannes Schaefer, Tharindu Ranasinghe, Marcos Zampieri, Durgesh Nandini, Amit Kumar Jaiswal
Date: 2021-12-17 00:00:00
Tasks: Binary Classification, Classification

The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.
Neural Architectures for Biological Inter-Sentence Relation Extraction
Authors: Enrique Noriega-Atala, Peter M. Lovett, Clayton T. Morrison, Mihai Surdeanu
Date: 2021-12-17 00:00:00
Tasks: Feature Engineering, Relation, Relation Extraction, Sentence

We introduce a family of deep-learning architectures for inter-sentence relation extraction, i.e., relations where the participants are not necessarily in the same sentence. We apply these architectures to an important use case in the biomedical domain: assigning biological context to biochemical events. In this work, biological context is defined as the type of biological system within which the biochemical event is observed. The neural architectures encode and aggregate multiple occurrences of the same candidate context mentions to determine whether it is the correct context for a particular event mention. We propose two broad types of architectures: the first type aggregates multiple instances that correspond to the same candidate context with respect to event mention before emitting a classification; the second type independently classifies each instance and uses the results to vote for the final class, akin to an ensemble approach. Our experiments show that the proposed neural classifiers are competitive and some achieve better performance than previous state of the art traditional machine learning methods without the need for feature engineering. Our analysis shows that the neural methods particularly improve precision compared to traditional machine learning classifiers and also demonstrates how the difficulty of inter-sentence relation extraction increases as the distance between the event and context mentions increase.
Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims
Authors: Qiang Sheng, Juan Cao, Xueyao Zhang, Xirong Li, Lei Zhong
Date: 2021-12-20 00:00:00
Proceeding: ACL 2021 5
Tasks: Articles, Fact Checking, Reranking, Sentence

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retrieved by BM25. However, these performances may be limited because they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics; (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. Models that ignore the two aspects only leverage semantic relevance and may be misled by sentences that describe similar but irrelevant events. In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. For event information, we propose a ROUGE-guided Transformer which is finetuned with regression of ROUGE. For pattern information, we generate pattern vectors for matching with sentences. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://github.com/ICTMCG/MTM.
Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach
Authors: Jingren Xu, Xin Kang, Ronghaixiang Zhang, Ying-Chang Liang, Sumei Sun
Date: 2021-12-20 00:00:00
Tasks: Deep Reinforcement Learning

This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the requirement on the computing capability of the UAV hardware, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only support single-agent learning and discrete actions.
3D-aware Image Synthesis via Learning Structural and Textural Representations
Authors: Yinghao Xu, Sida Peng, Ceyuan Yang, Yujun Shen, Bolei Zhou
Date: 2021-12-20 00:00:00
Proceeding: CVPR 2022 1
Tasks: 3D-Aware Image Synthesis, Generative Adversarial Network, Image Generation, NeRF

Making generative models 3D-aware bridges the 2D image space and the 3D physical world yet remains challenging. Recent attempts equip a Generative Adversarial Network (GAN) with a Neural Radiance Field (NeRF), which maps 3D coordinates to pixel values, as a 3D prior. However, the implicit function in NeRF has a very local receptive field, making the generator hard to become aware of the global structure. Meanwhile, NeRF is built on volume rendering which can be too costly to produce high-resolution results, increasing the optimization difficulty. To alleviate these two problems, we propose a novel framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation. We first learn a feature volume to represent the underlying structure, which is then converted to a feature field using a NeRF-like model. The feature field is further accumulated into a 2D feature map as the textural representation, followed by a neural renderer for appearance synthesis. Such a design enables independent control of the shape and the appearance. Extensive experiments on a wide range of datasets show that our approach achieves sufficiently higher image quality and better 3D control than the previous methods.
Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks
Authors: Zixuan Ke, Bing Liu, Xingchang Huang
Date: 2021-12-18 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Continual Learning

Existing research on continual learning of a sequence of tasks focused on dealing with catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared knowledge. Some work has also been done to transfer previously learned knowledge to the new task when the tasks are similar and have shared knowledge. To the best of our knowledge, no technique has been proposed to learn a sequence of mixed similar and dissimilar tasks that can deal with forgetting and also transfer knowledge forward and backward. This paper proposes such a technique to learn both types of tasks in the same network. For dissimilar tasks, the algorithm focuses on dealing with forgetting, and for similar tasks, the algorithm focuses on selectively transferring the knowledge learned from some similar previous tasks to improve the new task learning. Additionally, the algorithm automatically detects whether a new task is similar to any previous tasks. Empirical evaluation using sequences of mixed tasks demonstrates the effectiveness of the proposed model.
Continual Learning with Knowledge Transfer for Sentiment Classification
Authors: Zixuan Ke, Bing Liu, Hao Wang, Lei Shu
Date: 2021-12-18 00:00:00
Tasks: Classification, Continual Learning, Natural Questions, Sentiment Analysis, Sentiment Classification, Transfer Learning

This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the knowledge learned in the past from the previous tasks to the new task to help it learn a better model for the new task? And, can old models for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN can markedly improve the SC accuracy of both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of KAN is demonstrated through extensive experiments.
General Greedy De-bias Learning
Authors: Xinzhe Han, Shuhui Wang, Chi Su, Qingming Huang, Qi Tian
Date: 2021-12-20 00:00:00
Tasks: image-classification, Image Classification, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Neural networks often make predictions relying on the spurious correlations from the datasets rather than the intrinsic properties of the task of interest, facing sharp degradation on out-of-distribution (OOD) test data. Existing de-bias learning frameworks try to capture specific dataset bias by annotations but they fail to handle complicated OOD scenarios. Others implicitly identify the dataset bias by special design low capability biased models or losses, but they degrade when the training and testing data are from the same distribution. In this paper, we propose a General Greedy De-bias learning framework (GGD), which greedily trains the biased models and the base model. The base model is encouraged to focus on examples that are hard to solve with biased models, thus remaining robust against spurious correlations in the test stage. GGD largely improves models' OOD generalization ability on various tasks, but sometimes over-estimates the bias level and degrades on the in-distribution test. We further re-analyze the ensemble process of GGD and introduce the Curriculum Regularization inspired by curriculum learning, which achieves a good trade-off between in-distribution and out-of-distribution performance. Extensive experiments on image classification, adversarial question answering, and visual question answering demonstrate the effectiveness of our method. GGD can learn a more robust base model under the settings of both task-specific biased models with prior knowledge and self-ensemble biased model without prior knowledge.
Mask2Former for Video Instance Segmentation
Authors: Bowen Cheng, Anwesa Choudhuri, Ishan Misra, Alexander Kirillov, Rohit Girdhar, Alexander G. Schwing
Date: 2021-12-20 00:00:00
Tasks: Image Segmentation, Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation, Video Instance Segmentation, Video Segmentation, Video Semantic Segmentation

We find Mask2Former also achieves state-of-the-art performance on video instance segmentation without modifying the architecture, the loss or even the training pipeline. In this report, we show universal image segmentation architectures trivially generalize to video segmentation by directly predicting 3D segmentation volumes. Specifically, Mask2Former sets a new state-of-the-art of 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021. We believe Mask2Former is also capable of handling video semantic and panoptic segmentation, given its versatility in image segmentation. We hope this will make state-of-the-art video segmentation research more accessible and bring more attention to designing universal image and video segmentation architectures.
Human-vehicle Cooperative Visual Perception for Autonomous Driving under Complex Road and Traffic Scenarios
Authors: Yiyue Zhao, Cailin Lei, Yu Shen, Yuchuan Du, Qijun Chen
Date: 2021-12-17 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, object-detection, Object Detection, Transfer Learning

Human-vehicle cooperative driving has become the critical technology of autonomous driving, which reduces the workload of human drivers. However, the complex and uncertain road environments bring great challenges to the visual perception of cooperative systems. And the perception characteristics of autonomous driving differ from manual driving a lot. To enhance the visual perception capability of human-vehicle cooperative driving, this paper proposed a cooperative visual perception model. 506 images of complex road and traffic scenarios were collected as the data source. Then this paper improved the object detection algorithm of autonomous vehicles. The mean perception accuracy of traffic elements reached 75.52%. By the image fusion method, the gaze points of human drivers were fused with vehicles' monitoring screens. Results revealed that cooperative visual perception could reflect the riskiest zone and predict the trajectory of conflict objects more precisely. The findings can be applied in improving the visual perception algorithms and providing accurate data for planning and control.
Incomplete Knowledge Graph Alignment
Authors: Vinh Van Tong, Thanh Trung Huynh, Thanh Tam Nguyen, Hongzhi Yin, Quoc Viet Hung Nguyen, Quyet Thang Huynh
Date: 2021-12-17 00:00:00
Tasks: Graph Neural Network, Knowledge Graph Completion, Knowledge Graphs, Representation Learning

Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and completion. However, existing alignment techniques often assume that the input KGs are complete and isomorphic, which is not true due to the real-world heterogeneity in the domain, size, and sparsity. In this work, we address the problem of aligning incomplete KGs with representation learning. Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based. The former captures the consistency constraints between entities via translation paths, while the latter captures the neighbourhood structure of KGs via attention guided relation-aware graph neural network. The two feature channels are jointly learned to exchange important features between the input KGs while enforcing the output representations of the input KGs in the same embedding space. Also, we develop a missing links detector that discovers and recovers the missing links in the input KGs during the training process, which helps mitigate the incompleteness issue and thus improve the compatibility of the learned representations. The embeddings then are fused to generate the alignment result, and the high-confidence matched node pairs are updated to the pre-aligned supervision data to improve the embeddings gradually. Empirical results show that our model is more accurate than the SOTA and is robust against different levels of incompleteness.