pwc-archive/papers-with-abstracts huggingface dataset:
Deformable Part Descriptors for Fine-grained Recognition and Attribute Prediction
Authors: Ning Zhang, Ryan Farrell, Forrest Iandola, Trevor Darrell
Date: 2013-12-01 00:00:00
Proceeding: ICCV 2013 12
Tasks: Attribute, Fine-Grained Image Classification, Object Recognition

Recognizing objects in fine-grained domains can be
extremely challenging due to the subtle differences between subcategories. Discriminative markings are often
highly localized, leading traditional object recognition approaches to struggle with the large pose variation often
present in these domains. Pose-normalization seeks to align
training exemplars, either piecewise by part or globally
for the whole object, effectively factoring out differences
in pose and in viewing angle. Prior approaches relied
on computationally-expensive filter ensembles for part localization and required extensive supervision. This paper proposes two pose-normalized descriptors based on
computationally-efficient deformable part models. The
first leverages the semantics inherent in strongly-supervised
DPM parts. The second exploits weak semantic annotations to learn cross-component correspondences, computing pose-normalized descriptors from the latent parts of
a weakly-supervised DPM. These representations enable
pooling across pose and viewpoint, in turn facilitating tasks
such as fine-grained recognition and attribute prediction.
Experiments conducted on the Caltech-UCSD Birds 200
dataset and Berkeley Human Attribute dataset demonstrate
significant improvements over state-of-art algorithms.
libHOG: Energy-Efficient Histogram of Oriented Gradient Computation
Authors: Forrest Iandola, Matthew Moskewicz, Kurt Keutzer
Date: 2015-09-15 00:00:00
Proceeding: ITSC 2015 9
Tasks: Collision Avoidance, CPU, Object Detection

Histogram of Oriented Gradients (HOG) features
are the underlying representation in automotive computer vision
applications such as collision avoidance and lane keeping. In these
applications, we have observed that HOG feature computation
is often a slow and energy-intensive component of the overall
pipeline. In this paper, we focus on reducing both the time
taken and the energy used for computing Felzenszwalb HOG
features. We achieve our results though a combination of reduced
precision, SIMD parallelism, algorithmic changes, and outer-loop
parallelism. In particular, we address a bottleneck in histogram
accumulation by phrasing the problem as a gather instead of
the (traditional) scatter. Additionally, we explore the tradeoffs
of using L1 instead of L2 norms to compute gradients, which
enables smaller operands and more SIMD parallelism. Overall,
we are able to compute multiresolution HOG pyramids at 70fps
for 640x480 images on a multicore CPU. This is a 3.6x speedup
over the best known HOG implementation and a 29x speedup
over the popular voc-release5 HOG code. This is also a 3.6x -
22x reduction in energy per frame compared to previous HOG
implementations. Our open-source implementation is available
for download.
Deep learning based Chinese text sentiment mining and stock market correlation research
Authors: Chenrui Zhang
Date: 2022-05-10 00:00:00
Tasks: Deep Learning, Sentiment Analysis

We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.
A High Throughput Generative Vector Autoregression Model for Stochastic Synapses
Authors: T. Hennen, A. Elias, J. F. Nodin, G. Molas, R. Waser, D. J. Wouters, D. Bedau
Date: 2022-05-10 00:00:00
Tasks: 4k, Vocal Bursts Intensity Prediction

By imitating the synaptic connectivity and plasticity of the brain, emerging electronic nanodevices offer new opportunities as the building blocks of neuromorphic systems. One challenge for largescale simulations of computational architectures based on emerging devices is to accurately capture device response, hysteresis, noise, and the covariance structure in the temporal domain as well as between the different device parameters. We address this challenge with a high throughput generative model for synaptic arrays that is based on a recently available type of electrical measurement data for resistive memory cells. We map this real world data onto a vector autoregressive stochastic process to accurately reproduce the device parameters and their cross-correlation structure. While closely matching the measured data, our model is still very fast; we provide parallelized implementations for both CPUs and GPUs and demonstrate array sizes above one billion cells and throughputs exceeding one hundred million weight updates per second, above the pixel rate of a 30 frames/s 4K video stream.
Learning Non-target Knowledge for Few-shot Semantic Segmentation
Authors: Yuanwei Liu, Nian Liu, Qinglong Cao, Xiwen Yao, Junwei Han, Ling Shao
Date: 2022-05-10 00:00:00
Proceeding: CVPR 2022 1
Tasks: Contrastive Learning, Few-Shot Semantic Segmentation, Object, Segmentation, Semantic Segmentation

Existing studies in few-shot semantic segmentation only focus on mining the target object information, however, often are hard to tell ambiguous regions, especially in non-target regions, which include background (BG) and Distracting Objects (DOs). To alleviate this problem, we propose a novel framework, namely Non-Target Region Eliminating (NTRE) network, to explicitly mine and eliminate BG and DO regions in the query. First, a BG Mining Module (BGMM) is proposed to extract the BG region via learning a general BG prototype. To this end, we design a BG loss to supervise the learning of BGMM only using the known target object segmentation ground truth. Then, a BG Eliminating Module and a DO Eliminating Module are proposed to successively filter out the BG and DO information from the query feature, based on which we can obtain a BG and DO-free target object segmentation result. Furthermore, we propose a prototypical contrastive learning algorithm to improve the model ability of distinguishing the target object from DOs. Extensive experiments on both PASCAL-5i and COCO-20i datasets show that our approach is effective despite its simplicity.
OTFPF: Optimal Transport-Based Feature Pyramid Fusion Network for Brain Age Estimation with 3D Overlapped ConvNeXt
Authors: Yu Fu, Yanyan Huang, Yalin Wang, Shunjie Dong, Le Xue, Xunzhao Yin, Qianqian Yang, Yiyu Shi, Cheng Zhuo
Date: 2022-05-10 00:00:00
Tasks: Age Estimation

Chronological age of healthy brain is able to be predicted using deep neural networks from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as an effective biomarker for detecting aging-related diseases or disorders. In this paper, we propose an end-to-end neural network architecture, referred to as optimal transport based feature pyramid fusion (OTFPF) network, for the brain age estimation with T1 MRIs. The OTFPF consists of three types of modules: Optimal Transport based Feature Pyramid Fusion (OTFPF) module, 3D overlapped ConvNeXt (3D OL-ConvNeXt) module and fusion module. These modules strengthen the OTFPF network's understanding of each brain's semi-multimodal and multi-level feature pyramid information, and significantly improve its estimation performances. Comparing with recent state-of-the-art models, the proposed OTFPF converges faster and performs better. The experiments with 11,728 MRIs aged 3-97 years show that OTFPF network could provide accurate brain age estimation, yielding mean absolute error (MAE) of 2.097, Pearson's correlation coefficient (PCC) of 0.993 and Spearman's rank correlation coefficient (SRCC) of 0.989, between the estimated and chronological ages. Widespread quantitative experiments and ablation experiments demonstrate the superiority and rationality of OTFPF network. The codes and implement details will be released on GitHub: https://github.com/ZJU-Brain/OTFPF after final decision.
Informative knowledge distillation for image anomaly segmentation
Authors: Yunkang Cao, Qian Wan, Weiming Shen, Liang Gao
Date: 2022-07-19 00:00:00
Proceeding: Knowledge-Based Systems 2022 7
Tasks: Anomaly Detection, Anomaly Segmentation, Knowledge Distillation

Unsupervised anomaly segmentation methods based on knowledge distillation have recently been developed and show superior segmentation performance. However, rare attention has been paid to the overfitting problem caused by the inconsistency between the capacity of the neural network and the amount of knowledge in this scheme. This paper proposes a novel method named Informative Knowledge Distillation (IKD) to address the overfitting problem by increasing knowledge and offering a strong supervisory signal. Technically, a novel Context Similarity Loss (CSL) is proposed to capture context information from normal data manifolds. Besides, a novel Adaptive Hard Sample Mining (AHSM) is proposed to encourage more attention on hard samples with valuable information. With IKD, informative knowledge can be distilled, so that the overfitting problem can be well mitigated, and the performance can be further increased. The proposed method achieves better results on several categories of the well-known MVTec AD dataset than state-of-the-art methods in terms of AU-ROC, achieving 97.81% overall in 15 categories. Extensive experiments on ablation studies are also conducted to show the effectiveness of IKD in alleviating the overfitting problem.
Potential Game based Channel Allocation for Vehicular Edge Computing
Authors: XU Xin-cao, LIU Kai, LIU Chun-hui, Jiang Hao, GUO Song-tao, WU Wei-wei
Date: 2021-05-25 00:00:00
Proceeding: Acta Electronica Sinica 2021 5
Tasks: Edge-computing, global-optimization

In vehicular edge computing environments, the Co-channel interferences (CCI)is a critical problem when edge nodes allocate channels for different data transmission tasks. This article formulates the problem of channel allocation in vehicular edge computing, aiming at allocating sub-channels for different data transmission tasks and maximizing the ratio of successful data transmission. We transform the global optimization problem of channel allocation into a channel allocation potential game and prove the existence of nash equilibrium. We propose an Incentive-based probability update and strategy selection algorithm, which updates the strategy selection probability according to the incentive value of the selected strategy in each iteration and further analyzes the Nash equilibrium converge of the algorithm. Finally, we verify the convergence of the proposed algorithm and the effectiveness of the Nash equilibrium. The experimental results show that the proposed algorithm outperforms existing representative algorithms in terms of the ratio of successful data transmission and channel utilization efficiency.
Adaptive Graph Convolutional Network Framework for Multidimensional Time Series Prediction
Authors: Ning Wang
Date: 2022-05-08 00:00:00
Tasks: Graph Neural Network, Prediction, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

In the real world, long sequence time-series forecasting (LSTF) is needed in many cases, such as power consumption prediction and air quality prediction.Multi-dimensional long time series model has more strict requirements on the model, which not only needs to effectively capture the accurate long-term dependence between input and output, but also needs to capture the relationship between data of different dimensions.Recent research shows that the Informer model based on Transformer has achieved excellent performance in long time series prediction.However, this model still has some deficiencies in multidimensional prediction,it cannot capture the relationship between different dimensions well. We improved Informer to address its shortcomings in multidimensional forecasting. First,we introduce an adaptive graph neural network to capture hidden dimension dependencies in mostly time series prediction. Secondly,we integrate adaptive graph convolutional networks into various spatio-temporal series prediction models to solve the defect that they cannot capture the relationship between different dimensions. Thirdly,After experimental testing with multiple data sets, the accuracy of our framework improved by about 10\% after being introduced into the model.
Accelerating the Training of Video Super-Resolution Models
Authors: Lijian Lin, Xintao Wang, Zhongang Qi, Ying Shan
Date: 2022-05-10 00:00:00
Tasks: GPU, Super-Resolution, Video Super-Resolution

Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for good performance, resulting to slow training. However, is such a rigid training strategy necessary for VSR? In this work, we show that it is possible to gradually train video models from small to large spatial/temporal sizes, i.e., in an easy-to-hard manner. In particular, the whole training is divided into several stages and the earlier stage has smaller training spatial shape. Inside each stage, the temporal size also varies from short to long while the spatial size remains unchanged. Training is accelerated by such a multigrid training strategy, as most of computation is performed on smaller spatial and shorter temporal shapes. For further acceleration with GPU parallelization, we also investigate the large minibatch training without the loss in accuracy. Extensive experiments demonstrate that our method is capable of largely speeding up training (up to $6.2\times$ speedup in wall-clock training time) without performance drop for various VSR models. The code is available at https://github.com/TencentARC/Efficient-VSR-Training.
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Authors: Harmanpreet Kaur, Eytan Adar, Eric Gilbert, Cliff Lampe
Date: 2022-05-10 00:00:00

Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations for its outputs to aid human understanding. Though these approaches rely on guidelines for how humans explain things to each other, they ultimately solve for improving the artifact -- an explanation. In this paper, we propose an alternate framework for interpretability grounded in Weick's sensemaking theory, which focuses on who the explanation is intended for. Recent work has advocated for the importance of understanding stakeholders' needs -- we build on this by providing concrete properties (e.g., identity, social context, environmental cues, etc.) that shape human understanding. We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.
A Quantitative Symbolic Approach to Individual Human Reasoning
Authors: Emmanuelle Dietz, Johannes K. Fichte, Florim Hamiti
Date: 2022-05-10 00:00:00

Cognitive theories for reasoning are about understanding how humans come to conclusions from a set of premises. Starting from hypothetical thoughts, we are interested which are the implications behind basic everyday language and how do we reason with them. A widely studied topic is whether cognitive theories can account for typical reasoning tasks and be confirmed by own empirical experiments. This paper takes a different view and we do not propose a theory, but instead take findings from the literature and show how these, formalized as cognitive principles within a logical framework, can establish a quantitative notion of reasoning, which we call plausibility. For this purpose, we employ techniques from non-monotonic reasoning and computer science, namely, a solving paradigm called answer set programming (ASP). Finally, we can fruitfully use plausibility reasoning in ASP to test the effects of an existing experiment and explain different majority responses.
ALLSH: Active Learning Guided by Local Sensitivity and Hardness
Authors: Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, Mingyuan Zhou
Date: 2022-05-10 00:00:00
Proceeding: Findings (NAACL) 2022 7
Tasks: Active Learning, Few-Shot Learning, Sensitivity

Active learning, which effectively collects informative unlabeled data for annotation, reduces the demand for labeled data. In this work, we propose to retrieve unlabeled samples with a local sensitivity and hardness-aware acquisition function. The proposed method generates data copies through local perturbations and selects data points whose predictive likelihoods diverge the most from their copies. We further empower our acquisition function by injecting the select-worst case perturbation. Our method achieves consistent gains over the commonly used active learning strategies in various classification tasks. Furthermore, we observe consistent improvements over the baselines on the study of prompt selection in prompt-based few-shot learning. These experiments demonstrate that our acquisition guided by local sensitivity and hardness can be effective and beneficial for many NLP tasks.
Read the Room: Adapting a Robot's Voice to Ambient and Social Contexts
Authors: Paige Tuttosi, Emma Hughson, Akihiro Matsufuji, Angelica Lim
Date: 2022-05-10 00:00:00
Tasks: Speech Synthesis, Voice Conversion

How should a robot speak in a formal, quiet and dark, or a bright, lively and noisy environment? By designing robots to speak in a more social and ambient-appropriate manner we can improve perceived awareness and intelligence for these agents. We describe a process and results toward selecting robot voice styles for perceived social appropriateness and ambiance awareness. Understanding how humans adapt their voices in different acoustic settings can be challenging due to difficulties in voice capture in the wild. Our approach includes 3 steps: (a) Collecting and validating voice data interactions in virtual Zoom ambiances, (b) Exploration and clustering human vocal utterances to identify primary voice styles, and (c) Testing robot voice styles in recreated ambiances using projections, lighting and sound. We focus on food service scenarios as a proof-of-concept setting. We provide results using the Pepper robot's voice with different styles, towards robots that speak in a contextually appropriate and adaptive manner. Our results with N=120 participants provide evidence that the choice of voice style in different ambiances impacted a robot's perceived intelligence in several factors including: social appropriateness, comfort, awareness, human-likeness and competency.
Reasoning in the Description Logic ALC under Category Semantics
Authors: Ludovic Brieulle, Chan Le Duc, Pascal Vaillant
Date: 2022-05-10 00:00:00

We present in this paper a reformulation of the usual set-theoretical semantics of the description logic $\mathcal{ALC}$ with general TBoxes by using categorical language. In this setting, $\mathcal{ALC}$ concepts are represented as objects, concept subsumptions as arrows, and memberships as logical quantifiers over objects and arrows of categories. Such a category-based semantics provides a more modular representation of the semantics of $\mathcal{ALC}$. This feature allows us to define a sublogic of $\mathcal{ALC}$ by dropping the interaction between existential and universal restrictions, which would be responsible for an exponential complexity in space. Such a sublogic is undefinable in the usual set-theoretical semantics, We show that this sublogic is {\sc{PSPACE}} by proposing a deterministic algorithm for checking concept satisfiability which runs in polynomial space.
Strong Equivalence of Logic Programs with Ordered Disjunction: a Logical Perspective
Authors: Angelos Charalambidis, Christos Nomikos, Panos Rondogiannis
Date: 2022-05-10 00:00:00

Logic Programs with Ordered Disjunction (LPODs) extend classical logic programs with the capability of expressing preferential disjunctions in the heads of program rules. The initial semantics of LPODs, although simple and quite intuitive, is not purely model-theoretic. A consequence of this is that certain properties of programs appear non-trivial to formalize in purely logical terms. An example of this state of affairs is the characterization of the notion of strong equivalence for LPODs. Although the results of Faber et al. (2008) are accurately developed, they fall short of characterizing strong equivalence of LPODs as logical equivalence in some specific logic. This comes in sharp contrast with the well-known characterization of strong equivalence for classical logic programs, which, as proved by Lifschitz et al. (2001), coincides with logical equivalence in the logic of here-and-there. In this paper we obtain a purely logical characterization of strong equivalence of LPODs as logical equivalence in a four-valued logic. Moreover, we provide a new proof of the coNP-completeness of strong equivalence for LPODs, which has an interest in its own right since it relies on the special structure of such programs. Our results are based on the recent logical semantics of LPODs introduced by Charalambidis et al. (2021), a fact which we believe indicates that this new semantics may prove to be a useful tool in the further study of LPODs.
Scaling-up Generalized Planning as Heuristic Search with Landmarks
Authors: Javier Segovia-Aguas, Sergio Jiménez, Anders Jonsson, Laura Sebastiá
Date: 2022-05-10 00:00:00
Tasks: Heuristic Search

Landmarks are one of the most effective search heuristics for classical planning, but largely ignored in generalized planning. Generalized planning (GP) is usually addressed as a combinatorial search in a given space of algorithmic solutions, where candidate solutions are evaluated w.r.t.~the instances they solve. This type of solution evaluation ignores any sub-goal information that is not explicit in the representation of the planning instances, causing plateaus in the space of candidate generalized plans. Furthermore, node expansion in GP is a run-time bottleneck since it requires evaluating every child node over the entire batch of classical planning instances in a GP problem. In this paper we define a landmark counting heuristic for GP (that considers sub-goal information that is not explicitly represented in the planning instances), and a novel heuristic search algorithm for GP (that we call PGP) and that progressively processes subsets of the planning instances of a GP problem. Our two orthogonal contributions are analyzed in an ablation study, showing that both improve the state-of-the-art in GP as heuristic search, and that both benefit from each other when used in combination.