bluuebunny/crossref_metadata_2025 huggingface dataset:
Variational Autoencoder for Semi-Supervised Text Classification
Authors: Xu Weidi , Sun Haoze , Deng Chao , Tan Ying
Published: June 2022

Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder's capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two specific decoder structures are investigated and both of them are verified to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG's News corpus show that the proposed approach significantly improves the classification accuracy compared with pure-supervised classifiers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.
Deterministic Attention for Sequence-to-Sequence Constituent Parsing
Authors: Ma Chunpeng , Liu Lemao , Tamura Akihiro , Zhao Tiejun , Sumita Eiichiro
Published: June 2022

The sequence-to-sequence model is proven to be extremely successful in constituent parsing. It relies on one key technique, the probabilistic attention mechanism, to automatically select the context for prediction. Despite its successes, the probabilistic attention model does not always select the most important context. For example, the headword and boundary words of a subtree have been shown to be critical when predicting the constituent label of the subtree, but this contextual information becomes increasingly difficult to learn as the length of the sequence increases. In this study, we proposed a deterministic attention mechanism that deterministically selects the important context and is not affected by the sequence length. We implemented two different instances of this framework. When combined with a novel bottom-up linearization method, our parser demonstrated better performance than that achieved by the sequence-to-sequence parser with probabilistic attention mechanism.
Maximum Reconstruction Estimation for Generative Latent-Variable Models
Authors: Cheng Yong , Liu Yang , Xu Wei
Published: June 2022

Generative latent-variable models are important for natural language processing due to their capability of providing compact representations of data. As conventional maximum likelihood estimation (MLE) is prone to focus on explaining irrelevant but common correlations in data, we apply maximum reconstruction estimation (MRE) to learning generative latent-variable models alternatively, which aims to find model parameters that maximize the probability of reconstructing the observed data. We develop tractable algorithms to directly learn hidden Markov models and IBM translation models using the MRE criterion, without the need to introduce a separate reconstruction model to facilitate efficient inference. Experiments on unsupervised part-of-speech induction and unsupervised word alignment show that our approach enables generative latent-variable models to better discover intended correlations in data and outperforms maximum likelihood estimators significantly.
A Unified Model for Cross-Domain and Semi-Supervised Named Entity Recognition in Chinese Social Media
Authors: He Hangfeng , Sun Xu
Published: June 2022

Named entity recognition (NER) in Chinese social media is important but difficult because of its informality and strong noise. Previous methods only focus on in-domain supervised learning which is limited by the rare annotated data. However, there are enough corpora in formal domains and massive in-domain unannotated texts which can be used to improve the task. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated texts. The unified model contains two major functions. One is for cross-domain learning and another for semi-supervised learning. Cross-domain learning function can learn out-of-domain information based on domain similarity. Semi-Supervised learning function can learn in-domain unannotated information by self-training. Both learning functions outperform existing methods for NER in Chinese social media. Finally, our unified model yields nearly 11% absolute improvement over previously published results.
Bilingual Lexicon Induction from Non-Parallel Data with Minimal Supervision
Authors: Zhang Meng , Peng Haoruo , Liu Yang , Luan Huanbo , Sun Maosong
Published: June 2022

Building bilingual lexica from non-parallel data is a long-standing natural language processing research problem that could benefit thousands of resource-scarce languages which lack parallel data. Recent advances of continuous word representations have opened up new possibilities for this task, e.g. by establishing cross-lingual mapping between word embeddings via a seed lexicon. The method is however unreliable when there are only a limited number of seeds, which is a reasonable setting for resource-scarce languages. We tackle the limitation by introducing a novel matching mechanism into bilingual word representation learning. It captures extra translation pairs exposed by the seeds to incrementally improve the bilingual word embeddings. In our experiments, we find the matching mechanism to substantially improve the quality of the bilingual vector space, which in turn allows us to induce better bilingual lexica with seeds as few as 10.
Community-Based Question Answering via Asymmetric Multi-Faceted Ranking Network Learning
Authors: Zhao Zhou , Lu Hanqing , Zheng Vincent , Cai Deng , He Xiaofei , Zhuang Yueting
Published: June 2022

Nowadays the community-based question answering (CQA) sites become the popular Internet-based web service, which have accumulated millions of questions and their posted answers over time. Thus, question answering becomes an essential problem in CQA sites, which ranks the high-quality answers to the given question. Currently, most of the existing works study the problem of question answering based on the deep semantic matching model to rank the answers based on their semantic relevance, while ignoring the authority of answerers to the given question. In this paper, we consider the problem of community-based question answering from the viewpoint of asymmetric multi-faceted ranking network embedding. We propose a novel asymmetric multi-faceted ranking network learning framework for community-based question answering by jointly exploiting the deep semantic relevance between question-answer pairs and the answerers' authority to the given question. We then develop an asymmetric ranking network learning method with deep recurrent neural networks by integrating both answers' relative quality rank to the given question and the answerers' following relations in CQA sites. The extensive experiments on a large-scale dataset from a real world CQA site show that our method achieves better performance than other state-of-the-art solutions to the problem.
Salience Estimation via Variational Auto-Encoders for Multi-Document Summarization
Authors: Li Piji , Wang Zihao , Lam Wai , Ren Zhaochun , Bing Lidong
Published: June 2022

We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observed sentences and the corresponding latent semantic representations. Neural variational inference is used for the posterior inference of the latent variables. For salience estimation, we propose an unsupervised data reconstruction framework, which jointly considers the reconstruction for latent semantic space and observed term vector space. Therefore, we can capture the salience of sentences from these two different and complementary vector spaces. Thereafter, the VAEs-based latent semantic model is integrated into the sentence salience estimation component in a unified fashion, and the whole framework can be trained jointly by back-propagation via multi-task learning. Experimental results on the benchmark datasets DUC and TAC show that our framework achieves better performance than the state-of-the-art models.
Efficient Dependency-Guided Named Entity Recognition
Authors: Jie Zhanming , Muis Aldrian , Lu Wei
Published: June 2022

Named entity recognition (NER), which focuses on the extraction of semantically meaningful named entities and their semantic classes from text, serves as an indispensable component for several down-stream natural language processing (NLP) tasks such as relation extraction and event extraction. Dependency trees, on the other hand, also convey crucial semantic-level information. It has been shown previously that such information can be used to improve the performance of NER. In this work, we investigate on how to better utilize the structured information conveyed by dependency trees to improve the performance of NER. Specifically, unlike existing approaches which only exploit dependency information for designing local features, we show that certain global structured information of the dependency trees can be exploited when building NER models where such information can provide guided learning and inference. Through extensive experiments, we show that our proposed novel dependency-guided NER model performs competitively with models based on conventional semi-Markov conditional random fields, while requiring significantly less running time.
Using Discourse Signals for Robust Instructor Intervention Prediction
Authors: Chandrasekaran Muthu Kumar , Epp Carrie , Kan Min-Yen , Litman Diane
Published: June 2022

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relation-based features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.
Robust Execution of Probabilistic Temporal Plans
Authors: Lund Kyle , Dietrich Sam , Chow Scott , Boerkoel James
Published: June 2022

A critical challenge in temporal planning is robustly dealing with non-determinism, e.g., the durational uncertainty of a robot's activity due to slippage or other unexpected influences. Recent advances show that robustness is a better measure of solution quality than traditional metrics such as flexibility. This paper introduces the Robust Execution Problem for finding maximally robust dispatch strategies for general probabilistic temporal planning problems. While generally intractable, we introduce approximate solution techniques — one that can be computed statically prior to the start of execution with robustness guarantees and one that dynamically adjusts to opportunities and setbacks during execution. We show empirically that our dynamic approach outperforms all known approaches in terms of execution success rate.
Narrowing the Gap Between Saturated and Optimal Cost Partitioning for Classical Planning
Authors: Seipp Jendrik , Keller Thomas , Helmert Malte
Published: June 2022

In classical planning, cost partitioning is a method for admissibly combining a set of heuristic estimators by distributing operator costs among the heuristics. An optimal cost partitioning is often prohibitively expensive to compute. Saturated cost partitioning is an alternative that is much faster to compute and has been shown to offer high-quality heuristic guidance on Cartesian abstractions. However, its greedy nature makes it highly susceptible to the order in which the heuristics are considered. We show that searching in the space of orders leads to significantly better heuristic estimates than with previously considered orders. Moreover, using multiple orders leads to a heuristic that is significantly better informed than any single-order heuristic. In experiments with Cartesian abstractions, the resulting heuristic approximates the optimal cost partitioning very closely.
An Efficient Approach to Model-Based Hierarchical Reinforcement Learning
Authors: Li Zhuoru , Narayan Akshay , Leong Tze-Yun
Published: June 2022

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the state-of-the-art algorithms, and scales well in very large problems.
Accelerated Vector Pruning for Optimal POMDP Solvers
Authors: Walraven Erwin , Spaan Matthijs
Published: June 2022

Partially Observable Markov Decision Processes (POMDPs) are powerful models for planning under uncertainty in partially observable domains. However, computing optimal solutions for POMDPs is challenging because of the high computational requirements of POMDP solution algorithms. Several algorithms use a subroutine to prune dominated vectors in value functions, which requires a large number of linear programs (LPs) to be solved and it represents a large part of the total running time. In this paper we show how the LPs in POMDP pruning subroutines can be decomposed using a Benders decomposition. The resulting algorithm incrementally adds LP constraints and uses only a small fraction of the constraints. Our algorithm significantly improves the performance of existing pruning methods and the commonly used incremental pruning algorithm. Our new variant of incremental pruning is the fastest optimal pruning-based POMDP algorithm.
Fast SSP Solvers Using Short-Sighted Labeling
Authors: Pineda Luis , Wray Kyle , Zilberstein Shlomo
Published: June 2022

State-of-the-art methods for solving SSPs often work by limiting planning to restricted regions of the state space. The resulting problems can then be solved quickly, and the process is repeated during execution when states outside the restricted region are encountered. Typically, these approaches focus on states that are within some distance measure of the start state (e.g., number of actions or probability of being reached). However, these short-sighted approaches make it difficult to propagate information from states that are closer to a goal than to the start state, thus missing opportunities to improve planning. We present an alternative approach in which short-sightedness is used only to determine whether a state should be labeled as solved or not, but otherwise the set of states that can be accounted for during planning is unrestricted. Based on this idea, we propose the FLARES algorithm and show that it performs consistently well on a wide range of benchmark problems.
Latent Dirichlet Allocation for Unsupervised Activity Analysis on an Autonomous Mobile Robot
Authors: Duckworth Paul , Alomari Muhannad , Charles James , Hogg David , Cohn Anthony
Published: June 2022

For autonomous robots to collaborate on joint tasks with humans they require a shared understanding of an observed scene. We present a method for unsupervised learning of common human movements and activities on an autonomous mobile robot, which generalises and improves on recent results. Our framework encodes multiple qualitative abstractions of RGBD video from human observations and does not require external temporal segmentation. Analogously to information retrieval in text corpora, each human detection is modelled as a random mixture of latent topics. A generative probabilistic technique is used to recover topic distributions over an auto-generated vocabulary of discrete, qualitative spatio-temporal code words. We show that the emergent categories align well with human activities as interpreted by a human. This is a particularly challenging task on a mobile robot due to the varying camera viewpoints which lead to incomplete, partial and occluded human detections.
Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces
Authors: Camacho Alberto , Triantafillou Eleni , Muise Christian , Baier Jorge , McIlraith Sheila
Published: June 2022

Temporally extended goals are critical to the specification of a diversity of real-world planning problems. Here we examine the problem of non-deterministic planning with temporally extended goals specified in linear temporal logic (LTL), interpreted over either finite or infinite traces. Unlike existing LTL planners, we place no restrictions on our LTL formulae beyond those necessary to distinguish finite from infinite interpretations. We generate plans by compiling LTL temporally extended goals into problem instances described in the Planning Domain Definition Language that are solved by a state-of-the-art fully observable non-deterministic planner. We propose several different compilations based on translations of LTL to (Büchi) alternating or (Büchi) non-deterministic finite state automata, and evaluate various properties of the competing approaches. We address a diverse spectrum of LTL planning problems that, to this point, had not been solvable using AI planning techniques, and do so in a manner that demonstrates highly competitive performance.
Extracting Highly Effective Features for Supervised Learning via Simultaneous Tensor Factorization
Authors: Verma Sunny , Liu Wei , Wang Chen , Zhu Liming
Published: June 2022

Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.
Auto-Annotation of 3D Objects via ImageNet
Authors: Luo Huan , Wang Cheng , Li Jonathan
Published: June 2022

Automatic annotation of 3D objects in cluttered scenes shows its great importance to a variety of applications. Nowadays, 3D point clouds, a new 3D representation of real-world objects, can be easily and rapidly collected by mobile LiDAR systems, e.g. RIEGL VMX-450 system. Moreover, the mobile LiDAR system can also provide a series of consecutive multi-view images which are calibrated with 3D point clouds. This paper proposes to automatically annotate 3D objects of interest in point clouds of road scenes by exploiting a multitude of annotated images in image databases, such as LabelMe and ImageNet. In the proposed method, an object detector trained on the annotated images is used to locate the object regions in acquired multi-view images. Then, based on the correspondences between multi-view images and 3D point clouds, a probabilistic graphical model is used to model the temporal, spatial and geometric constraints to extract the 3D objects automatically. A new dataset was built for evaluation and the experimental results demonstrate a satisfied performance on 3D object extraction.
Android Malware Detection with Weak Ground Truth Data
Authors: DeLoach Jordan , Caragea Doina , Ou Xinming
Published: June 2022

For Android malware detection, precise ground truth is a rare commodity. As security knowledge evolves, what may be considered ground truth at one moment in time may change, and apps once considered benign may turn out to be malicious. The inevitable noise in data labels poses a challenge to inferring effective machine learning classifiers. Our work is focused on approaches for learning classifiers for Android malware detection in a manner that is methodologically sound with regard to the uncertain and ever-changing ground truth in the problem space. We leverage the fact that although data labels are unavoidably noisy, a malware label is much more precise than a benign label. While you can be confident that an app is malicious, you can never be certain that a benign app is really benign, or just undetected malware. Based on this insight, we leverage a modified Logistic Regression classifier that allows us to learn from only positive and unlabeled data, without making any assumptions about benign labels. We find Label Regularized Logistic Regression to perform well for noisy app datasets, as well as datasets where there is a limited amount of positive labeled data, both of which are representative of real-world situations.
Robust Stable Marriage
Authors: Genc Begum , Siala Mohamed , O'Sullivan Barry , Simonin Gilles
Published: June 2022

Stable Marriage (SM) is a well-known matching problem, where the aim is to match a set of men and women. The resulting matching must satisfy two properties: there is no unassigned person and there are no other assignments where two people of opposite gender prefer each other to their current assignments. We propose a new version of SM called as Robust Stable Marriage (RSM) by combining stability and robustness. We define robustness by introducing (a,b)-supermatches, which has been inspired by (a,b)-supermodels. An (a,b)-supermatch is a stable matching, where if at most a pairs want to break up, it is possible to find another stable matching by breaking at most b other pairs.
Evolutionary Machine Learning for RTS Game StarCraft
Authors: Wu Lianlong , Markham Andrew
Published: June 2022

Real-Time Strategy (RTS) games involve multiple agents acting simultaneously, and result in enormous state dimensionality. In this paper, we propose an abstracted and simplified model for the famous game StarCraft, and design a dynamic programming algorithm to solve the building order problem, which takes minimal time to achieve a specific target. In addition, Genetic Algorithms (GA) are used to find an optimal target for the opening stage.
Multimodal Fusion of EEG and Musical Features in Music-Emotion Recognition
Authors: Thammasan Nattapong , Fukui Ken-ichi , Numao Masayuki
Published: June 2022

Multimodality has been recently exploited to overcome the challenges of emotion recognition. In this paper, we present a study of fusion of electroencephalogram (EEG) features and musical features extracted from musical stimuli at decision level in recognizing the time-varying binary classes of arousal and valence. Our empirical results demonstrate that EEG modality was suffered from the non-stability of EEG signals, yet fusing with music modality could alleviate the issue and enhance the performance of emotion recognition.
CoCoA: A Non-Iterative Approach to a Local Search (A)DCOP Solver
Authors: Van Leeuwen Cornelis Jan , Pawelczak Przemyslaw
Published: June 2022

We propose a novel incomplete cooperative algorithm for distributed constraint optimization problems (DCOPs) denoted as Cooperative Constraint Approximation (CoCoA). The key strategy of the algorithm is to use a semi-greedy approach in which knowledge is distributed amongst neighboring agents, and assigning a value only once instead of an iterative approach. Furthermore, CoCoA uses a unique-first approach to improve the solution quality. It is designed such that it can solve DCOPs as well as Asymmetric DCOPS, with only few messages being communicated between neighboring agents. Experimentally, through evaluating graph coloring problems, randomized (A)DCOPs, and a sensor network communication problem, we show that CoCoA is able to very quickly find solutions of high quality with a smaller communication overhead than state-of-the-art DCOP solvers such as DSA, MGM-2, ACLS, MCS-MGM and Max-Sum. In our asymmetric use case problem of a sensor network, we show that CoCoA not only finds the best solution, but also finds this solution faster than any other algorithm.
Algorithms for Deciding Counting Quantifiers over Unary Predicates
Authors: Finger Marcelo , De Bona Glauber
Published: June 2022

We study algorithms for fragments of first order logic ex- tended with counting quantifiers, which are known to be highly complex in general. We propose a fragment over unary predicates that is NP-complete and for which there is a nor- mal form where Counting Quantification sentences have a single Unary predicate, thus call it the CQU fragment. We provide an algebraic formulation of the CQU satisfiability problem in terms of Integer Linear Programming based on which two algorithms are proposed, a direct reduction to SAT instances and an Integer Linear Programming version extended with a column generation mechanism. The latter is shown to lead to a viable implementation and experiments shows this algorithm presents a phase transition behavior.
The Opacity of Backbones
Authors: Hemaspaandra Lane , Narváez David
Published: June 2022

A backbone of a boolean formula F is a collection S of its variables for which there is a unique partial assignment aS such that F[aS] is satisfiable (Monasson et al. 1999; Williams, Gomes, and Selman 2003).  This paper studies the nontransparency of backbones.  We show that, under the widely believed assumption that integer factoring is hard, there exist sets of boolean formulas that have obvious, nontrivial backbones yet finding the values, aS, of those backbones is intractable.  We also show that, under the same assumption, there exist sets of boolean formulas that obviously have large backbones yet producing such a backbone S is intractable.  Further, we show that if integer factoring is not merely worst-case hard but is frequently hard, as is widely believed, then the frequency of hardness in our two results is not too much less than that frequency.
The AI Rebellion: Changing the Narrative
Authors: Aha David , Coman Alexandra
Published: June 2022

Sci-fi narratives permeating the collective consciousness endow AI Rebellion with ample negative connotations. However, for AI agents, as for humans, attitudes of protest, objection, and rejection have many potential benefits in support of ethics, safety, self-actualization, solidarity, and social justice, and are necessary in a wide variety of contexts. We launch a conversation on constructive AI rebellion and describe a framework meant to support discussion, implementation, and deployment of AI Rebel Agents as protagonists of positive narratives.
Strategic Social Network Analysis
Authors: Michalak Tomasz , Rahwan Talal , Wooldridge Michael
Published: June 2022

How can individuals and communities protect their privacy against social network analysis tools? How do criminals or terrorists organizations evade detection by such tools? Under which conditions can these tools be made strategy proof? These fundamental questions have attracted little attention in the literature to date, as most social network analysis tools are built around the assumption that individuals or groups in a network do not act strategically to evade such tools. With this in mind, we outline in this paper a new paradigm for social network analysis, whereby the strategic behaviour of network actors is explicitly modeled. Addressing this research challenge has various implications. For instance, it may allow two individuals to keep their relationship secret or private. It may also allow members of an activist group to conceal their membership, or even conceal the existence of their group from authoritarian regimes. Furthermore, it may assist security agencies and counter terrorism units in understanding the strategies that covert organizations use to escape detection, and give rise to new strategy-proof countermeasures.
Imagined Visual Representations as Multimodal Embeddings
Authors: Collell Guillem , Zhang Ted , Moens Marie-Francine
Published: June 2022

Language and vision provide complementary information. Integrating both modalities in a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision. In this paper, we present a simple and effective method that learns a language-to-vision mapping and uses its output visual predictions to build multimodal representations. In this sense, our method provides a cognitively plausible way of building representations, consistent with the inherently re-constructive and associative nature of human memory. Using seven benchmark concept similarity tests we show that the mapped (or imagined) vectors not only help to fuse multimodal information, but also outperform strong unimodal baselines and state-of-the-art multimodal methods, thus exhibiting more human-like judgments. Ultimately, the present work sheds light on fundamental questions of natural language understanding concerning the fusion of vision and language such as the plausibility of more associative and re-constructive approaches.
Dynamic Optimization of Landscape Connectivity Embedding Spatial-Capture-Recapture Information
Authors: Xue Yexiang , Wu Xiaojian , Morin Dana , Dilkina Bistra , Fuller Angela , Royle J. , Gomes Carla
Published: June 2022

Maintaining landscape connectivity is increasingly important in wildlife conservation, especially for species experiencing the effects of habitat loss and fragmentation. We propose a novel approach to dynamically optimize landscape connectivity. Our approach is based on a mixed integer program formulation, embedding a spatial capture-recapture model that estimates the density, space usage, and landscape connectivity for a given species. Our method takes into account the fact that local animal density and connectivity change dynamically and non-linearly with different habitat protection plans. In order to scale up our encoding, we propose a sampling scheme via random partitioning of the search space using parity functions. We show that our method scales to real-world size problems and dramatically outperforms the solution quality of an expectation maximization approach and a sample average approximation approach.
Spatial Projection of Multiple Climate Variables Using Hierarchical Multitask Learning
Authors: Goncalves Andre , Banerjee Arindam , Von Zuben Fernando
Published: June 2022

Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask learning (MTL) framework for projections of individual climate variables. In this paper we introduce a framework for hierarchical multitask learning (HMTL) with two levels of tasks such that each super-task, i.e., task at the top level, is itself a multitask learning problem over sub-tasks. For climate projections, each super-task focuses on projections of specific climate variables spatially using an MTL formulation. For the proposed HMTL approach, a group lasso regularization is added to couple parameters across the super-tasks, which in the climate context helps exploit relationships among the behavior of different climate variables at a given spatial location. We show that some recent works on MTL based on learning task dependency structures can be viewed as special cases of HMTL. Experiments on synthetic and real climate data show that HMTL produces better results than decoupled MTL methods applied separately on the super-tasks and HMTL significantly outperforms baselines for climate projection.
State Projection via AI Planning
Authors: Sohrabi Shirin , Riabov Anton , Udrea Octavian
Published: June 2022

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.
Zero-Shot Recognition via Direct Classifier Learning with Transferred Samples and Pseudo Labels
Authors: Guo Yuchen , Ding Guiguang , Han Jungong , Gao Yue
Published: June 2022

As an interesting and emerging topic, zero-shot recognition (ZSR) makes it possible to train a recognition model by specifying the category's attributes when there are no labeled exemplars available. The fundamental idea for ZSR is to transfer knowledge from the abundant labeled data in different but related source classes via the class attributes. Conventional ZSR approaches adopt a two-step strategy in test stage, where the samples are projected into the attribute space in the first step, and then the recognition is carried out based on considering the relationship between samples and classes in the attribute space. Due to this intermediate transformation, information loss is unavoidable, thus degrading the performance of the overall system. Rather than following this two-step strategy, in this paper, we propose a novel one-step approach that is able to perform ZSR in the original feature space by using directly trained classifiers. To tackle the problem that no labeled samples of target classes are available, we propose to assign pseudo labels to samples based on the reliability and diversity, which in turn will be used to train the classifiers. Moreover, we adopt a robust SVM that accounts for the unreliability of pseudo labels. Extensive experiments on four datasets demonstrate consistent performance gains of our approach over the state-of-the-art two-step ZSR approaches.
Non-Rigid Point Set Registration with Robust Transformation Estimation under Manifold Regularization
Authors: Ma Jiayi , Zhao Ji , Jiang Junjun , Zhou Huabing
Published: June 2022

In this paper, we propose a robust transformation estimation method based on manifold regularization for non-rigid point set registration. The method iteratively recovers the point correspondence and estimates the spatial transformation between two point sets. The correspondence is established based on existing local feature descriptors which typically results in a number of outliers. To achieve an accurate estimate of the transformation from such putative point correspondence, we formulate the registration problem by a mixture model with a set of latent variables introduced to identify outliers, and a prior involving manifold regularization is imposed on the transformation to capture the underlying intrinsic geometry of the input data. The non-rigid transformation is specified in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on both 2D and 3D data demonstrate that our method can yield superior results compared to other state-of-the-arts, especially in case of badly degraded data.
Reference Based LSTM for Image Captioning
Authors: Chen Minghai , Ding Guiguang , Zhao Sicheng , Chen Hui , Liu Qiang , Han Jungong
Published: June 2022

Image captioning is an important problem in artificial intelligence, related to both computer vision and natural language processing. There are two main problems in existing methods: in the training phase, it is difficult to find which parts of the captions are more essential to the image; in the caption generation phase, the objects or the scenes are sometimes misrecognized. In this paper, we consider the training images as the references and propose a Reference based Long Short Term Memory (R-LSTM) model, aiming to solve these two problems in one goal. When training the model, we assign different weights to different words, which enables the network to better learn the key information of the captions. When generating a caption, the consensus score is utilized to exploit the reference information of neighbor images, which might fix the misrecognition and make the descriptions more natural-sounding. The proposed R-LSTM model outperforms the state-of-the-art approaches on the benchmark dataset MS COCO and obtains top 2 position on 11 of the 14 metrics on the online test server.
Learning Patch-Based Dynamic Graph for Visual Tracking
Authors: Li Chenglong , Lin Liang , Zuo Wangmeng , Tang Jin
Published: June 2022

Existing visual tracking methods usually localize the object with a bounding box, in which the foreground object trackers/detectors are often disturbed by the introduced background information. To handle this problem, we aim to learn a more robust object representation for visual tracking. In particular, the tracked object is represented with a graph structure (i.e., a set of non-overlapping image patches), in which the weight of each node (patch) indicates how likely it belongs to the foreground and edges are also weighed for indicating the appearance compatibility of two neighboring nodes. This graph is dynamically learnt (i.e., the nodes and edges received weights) and applied in object tracking and model updating. We constrain the graph learning from two aspects: i) the global low-rank structure over all nodes and ii) the local sparseness of node neighbors. During the tracking process, our method performs the following steps at each frame. First, the graph is initialized by assigning either 1 or 0 to the weights of some image patches according to the predicted bounding box. Second, the graph is optimized through designing a new ALM (Augmented Lagrange Multiplier) based algorithm. Third, the object feature representation is updated by imposing the weights of patches on the extracted image features. The object location is finally predicted by adopting the Struck tracker. Extensive experiments show that our approach outperforms the state-of-the-art tracking methods on two standard benchmarks, i.e., OTB100 and NUS-PRO.
Localizing by Describing: Attribute-Guided Attention Localization for Fine-Grained Recognition
Authors: Liu Xiao , Wang Jiang , Wen Shilei , Ding Errui , Lin Yuanqing
Published: June 2022

A key challenge in fine-grained recognition is how to find and represent discriminative local regions.Recent attention models are capable of learning discriminative region localizers only from category labels with reinforcement learning. However, not utilizing any explicit part information, they are not able to accurately find multiple distinctive regions.In this work, we introduce an attribute-guided attention localization scheme where the local region localizers are learned under the guidance of part attribute descriptions.By designing a novel reward strategy, we are able to learn to locate regions that are spatially and semantically distinctive with reinforcement learning algorithm. The attribute labeling requirement of the scheme is more amenable than the accurate part location annotation required by traditional part-based fine-grained recognition methods.Experimental results on the CUB-200-2011 dataset demonstrate the superiority of the proposed scheme on both fine-grained recognition and attribute recognition.
Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models
Authors: Li Siyi , Yeung Dit-Yan
Published: June 2022

Despite recent advances in the visual tracking community, most studies so far have focused on the observation model. As another important component in the tracking system, the motion model is much less well-explored especially for some extreme scenarios. In this paper, we consider one such scenario in which the camera is mounted on an unmanned aerial vehicle (UAV) or drone. We build a benchmark dataset of high diversity, consisting of 70 videos captured by drone cameras. To address the challenging issue of severe camera motion, we devise simple baselines to model the camera motion by geometric transformation based on background feature points. An extensive comparison of recent state-of-the-art trackers and their motion model variants on our drone tracking dataset validates both the necessity of the dataset and the effectiveness of the proposed methods. Our aim for this work is to lay the foundation for further research in the UAV tracking area.
Robust Visual Tracking via Local-Global Correlation Filter
Authors: Fan Heng , Xiang Jinhai
Published: June 2022

Correlation filter has drawn increasing interest in visual tracking due to its high efficiency, however, it is sensitive to partial occlusion, which may result in tracking failure. To address this problem, we propose a novel local-global correlation filter (LGCF) for object tracking. Our LGCF model utilizes both local-based and global-based strategies, and effectively combines these two strategies by exploiting the relationship of circular shifts among local object parts and global target for their motion models to preserve the structure of object. In specific, our proposed model has two advantages: (1) Owing to the benefits of local-based mechanism, our method is robust to partial occlusion by leveraging visible parts. (2) Taking into account the relationship of motion models among local parts and global target, our LGCF model is able to capture the inner structure of object, which further improves its robustness to occlusion. In addition, to alleviate the issue of drift away from object, we incorporate temporal consistencies of both local parts and global target in our LGCF model. Besides, we adopt an adaptive method to accurately estimate the scale of object. Extensive experiments on OTB15 with 100 videos demonstrate that our tracking algorithm performs favorably against state-of-the-art methods.
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Authors: Song Sijie , Lan Cuiling , Xing Junliang , Zeng Wenjun , Liu Jiaying
Published: June 2022

Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model, both on the small human action recognition dataset of SBU and the currently largest NTU dataset.
Depth CNNs for RGB-D Scene Recognition: Learning from Scratch Better than Transferring from RGB-CNNs
Authors: Song Xinhang , Herranz Luis , Jiang Shuqiang
Published: June 2022

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.
Learning Discriminative Activated Simplices for Action Recognition
Authors: Luo Chenxu , Ma Chang , Wang Chunyu , Wang Yizhou
Published: June 2022

We address the task of action recognition from a sequence of 3D human poses. This is a challenging task firstly because the poses of the same class could have large intra-class variations either caused by inaccurate 3D pose estimation or various performing styles. Also different actions, e.g., walking vs. jogging, may share similar poses which makes the representation not discriminative to differentiate the actions. To solve the problems, we propose a novel representation for 3D poses by a mixture of Discriminative Activated Simplices (DAS). Each DAS consists of a few bases and represent pose data by their convex combinations. The discriminative power of DAS is firstly realized by learning discriminative bases across classes with a block diagonal constraint enforced on the basis coefficient matrix. Secondly, the DAS provides tight characterization of the pose manifolds thus reducing the chance of generating overlapped DAS between similar classes. We justify the power of the model on benchmark datasets and witness consistent performance improvements.