pwc-archive/papers-with-abstracts huggingface dataset:
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision
Authors: Gaurav Bhatt, Shivam Chandhok, Vineeth N Balasubramanian
Date: 2021-07-11 00:00:00
Tasks: Few-Shot Learning, Generalized Zero-Shot Learning, Zero-Shot Learning

A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize unlabeled images from unseen classes (i.e transductive zero-shot) during training to enable generalization. However, this limits their use in practical scenarios where data from target unseen classes is unavailable or infeasible to collect. In this work, we present a practical setting of inductive zero and few-shot learning, where unlabeled images from other out-of-data classes, that do not belong to seen or unseen categories, can be used to improve generalization in any-shot learning. We leverage a formulation based on product-of-experts and introduce a new AUD module that enables us to use unlabeled samples from out-of-data classes which are usually easily available and practically entail no annotation cost. In addition, we also demonstrate the applicability of our model to address a more practical and challenging, Generalized Zero-shot under a limited supervision setting, where even base seen classes do not have sufficient annotated samples.
Partial Video Domain Adaptation with Partial Adversarial Temporal Attentive Network
Authors: Yuecong Xu, Jianfei Yang, Haozhi Cao, Qi Li, Kezhi Mao, Zhenghua Chen
Date: 2021-07-11 00:00:00
Proceeding: ICCV 2021 10
Tasks: Domain Adaptation, Partial Domain Adaptation

Partial Domain Adaptation (PDA) is a practical and general domain adaptation scenario, which relaxes the fully shared label space assumption such that the source label space subsumes the target one. The key challenge of PDA is the issue of negative transfer caused by source-only classes. For videos, such negative transfer could be triggered by both spatial and temporal features, which leads to a more challenging Partial Video Domain Adaptation (PVDA) problem. In this paper, we propose a novel Partial Adversarial Temporal Attentive Network (PATAN) to address the PVDA problem by utilizing both spatial and temporal features for filtering source-only classes. Besides, PATAN constructs effective overall temporal features by attending to local temporal features that contribute more toward the class filtration process. We further introduce new benchmarks to facilitate research on PVDA problems, covering a wide range of PVDA scenarios. Empirical results demonstrate the state-of-the-art performance of our proposed PATAN across the multiple PVDA benchmarks.
Aligning Correlation Information for Domain Adaptation in Action Recognition
Authors: Yuecong Xu, Jianfei Yang, Haozhi Cao, Kezhi Mao, Jianxiong Yin, Simon See
Date: 2021-07-11 00:00:00
Tasks: Action Recognition, Domain Adaptation, Video Classification

Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research towards video DA. This is partly due to the complexity in adapting the different modalities of features in videos, which includes the correlation features extracted as long-term dependencies of pixels across spatiotemporal dimensions. The correlation features are highly associated with action classes and proven their effectiveness in accurate video feature extraction through the supervised action recognition task. Yet correlation features of the same action would differ across domains due to domain shift. Therefore we propose a novel Adversarial Correlation Adaptation Network (ACAN) to align action videos by aligning pixel correlations. ACAN aims to minimize the distribution of correlation information, termed as Pixel Correlation Discrepancy (PCD). Additionally, video DA research is also limited by the lack of cross-domain video datasets with larger domain shifts. We, therefore, introduce a novel HMDB-ARID dataset with a larger domain shift caused by a larger statistical difference between domains. This dataset is built in an effort to leverage current datasets for dark video classification. Empirical results demonstrate the state-of-the-art performance of our proposed ACAN for both existing and the new video DA datasets.
Distributed Deep Reinforcement Learning for Intelligent Traffic Monitoring with a Team of Aerial Robots
Authors: Behzad Khamidehi, Elvino S. Sousa
Date: 2021-07-10 00:00:00
Tasks: Deep Reinforcement Learning, Management, Reinforcement Learning (RL)

This paper studies the traffic monitoring problem in a road network using a team of aerial robots. The problem is challenging due to two main reasons. First, the traffic events are stochastic, both temporally and spatially. Second, the problem has a non-homogeneous structure as the traffic events arrive at different locations of the road network at different rates. Accordingly, some locations require more visits by the robots compared to other locations. To address these issues, we define an uncertainty metric for each location of the road network and formulate a path planning problem for the aerial robots to minimize the network's average uncertainty. We express this problem as a partially observable Markov decision process (POMDP) and propose a distributed and scalable algorithm based on deep reinforcement learning to solve it. We consider two different scenarios depending on the communication mode between the agents (aerial robots) and the traffic management center (TMC). The first scenario assumes that the agents continuously communicate with the TMC to send/receive real-time information about the traffic events. Hence, the agents have global and real-time knowledge of the environment. However, in the second scenario, we consider a challenging setting where the observation of the aerial robots is partial and limited to their sensing ranges. Moreover, in contrast to the first scenario, the information exchange between the aerial robots and the TMC is restricted to specific time instances. We evaluate the performance of our proposed algorithm in both scenarios for a real road network topology and demonstrate its functionality in a traffic monitoring system.
From Common Sense Reasoning to Neural Network Models through Multiple Preferences: an overview
Authors: Laura Giordano, Valentina Gliozzi, Daniele Theseider Dupré
Date: 2021-07-10 00:00:00
Tasks: Common Sense Reasoning

In this paper we discuss the relationships between conditional and preferential logics and neural network models, based on a multi-preferential semantics. We propose a concept-wise multipreference semantics, recently introduced for defeasible description logics to take into account preferences with respect to different concepts, as a tool for providing a semantic interpretation to neural network models. This approach has been explored both for unsupervised neural network models (Self-Organising Maps) and for supervised ones (Multilayer Perceptrons), and we expect that the same approach might be extended to other neural network models. It allows for logical properties of the network to be checked (by model checking) over an interpretation capturing the input-output behavior of the network. For Multilayer Perceptrons, the deep network itself can be regarded as a conditional knowledge base, in which synaptic connections correspond to weighted conditionals. The paper describes the general approach, through the cases of Self-Organising Maps and Multilayer Perceptrons, and discusses some open issues and perspectives.
Dense-Sparse Deep Convolutional Neural Networks Training for Image Denoising
Authors: Basit O. Alawode, Mudassir Masood
Date: 2021-07-10 00:00:00
Tasks: Denoising, Image Denoising

Recently, deep learning methods such as the convolutional neural networks have gained prominence in the area of image denoising. This is owing to their proven ability to surpass state-of-the-art classical image denoising algorithms such as block-matching and 3D filtering algorithm. Deep denoising convolutional neural networks use many feed-forward convolution layers with added regularization methods of batch normalization and residual learning to speed up training and improve denoising performance significantly. However, this comes at the expense of a huge number of trainable parameters. In this paper, we show that by employing an enhanced dense-sparse-dense network training procedure to the deep denoising convolutional neural networks, comparable denoising performance level can be achieved at a significantly reduced number of trainable parameters. We derive motivation from the fact that networks trained using the dense-sparse-dense approach have been shown to attain performance boost with reduced number of parameters. The proposed reduced deep denoising convolutional neural networks network is an efficient denoising model with significantly reduced parameters and comparable performance to the deep denoising convolutional neural networks. Additionally, denoising was achieved at significantly reduced processing time.
Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls
Authors: Helmut Wasserbacher, Martin Spindler
Date: 2021-07-10 00:00:00
Tasks: BIG-bench Machine Learning, Causal Inference

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP\&A). Machine learning appears well suited to support FP\&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP\&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.
Propagation-aware Social Recommendation by Transfer Learning
Authors: Haodong Chang, Yabo Chu
Date: 2021-07-10 00:00:00
Tasks: Recommendation Systems, Transfer Learning

Social-aware recommendation approaches have been recognized as an effective way to solve the data sparsity issue of traditional recommender systems. The assumption behind is that the knowledge in social user-user connections can be shared and transferred to the domain of user-item interactions, whereby to help learn user preferences. However, most existing approaches merely adopt the first-order connections among users during transfer learning, ignoring those connections in higher orders. We argue that better recommendation performance can also benefit from high-order social relations. In this paper, we propose a novel Propagation-aware Transfer Learning Network (PTLN) based on the propagation of social relations. We aim to better mine the sharing knowledge hidden in social networks and thus further improve recommendation performance. Specifically, we explore social influence in two aspects: (a) higher-order friends have been taken into consideration by order bias; (b) different friends in the same order will have distinct importance for recommendation by an attention mechanism. Besides, we design a novel regularization to bridge the gap between social relations and user-item interactions. We conduct extensive experiments on two real-world datasets and beat other counterparts in terms of ranking accuracy, especially for the cold-start users with few historical interactions.
Identifying Layers Susceptible to Adversarial Attacks
Authors: Shoaib Ahmed Siddiqui, Thomas Breuel
Date: 2021-07-10 00:00:00
Tasks: Dimensionality Reduction

In this paper, we investigate the use of pretraining with adversarial networks, with the objective of discovering the relationship between network depth and robustness. For this purpose, we selectively retrain different portions of VGG and ResNet architectures on CIFAR-10, Imagenette, and ImageNet using non-adversarial and adversarial data. Experimental results show that susceptibility to adversarial samples is associated with low-level feature extraction layers. Therefore, retraining of high-level layers is insufficient for achieving robustness. Furthermore, adversarial attacks yield outputs from early layers that differ statistically from features for non-adversarial samples and do not permit consistent classification by subsequent layers. This supports common hypotheses regarding the association of robustness with the feature extractor, insufficiency of deeper layers in providing robustness, and large differences in adversarial and non-adversarial feature vectors.
Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition
Authors: Fangqiu Yi, Tingting Jiang
Date: 2021-07-10 00:00:00
Tasks: Online surgical phase recognition, Surgical phase recognition

Surgical phase recognition is of particular interest to computer assisted surgery systems, in which the goal is to predict what phase is occurring at each frame for a surgery video. Networks with multi-stage architecture have been widely applied in many computer vision tasks with rich patterns, where a predictor stage first outputs initial predictions and an additional refinement stage operates on the initial predictions to perform further refinement. Existing works show that surgical video contents are well ordered and contain rich temporal patterns, making the multi-stage architecture well suited for the surgical phase recognition task. However, we observe that when simply applying the multi-stage architecture to the surgical phase recognition task, the end-to-end training manner will make the refinement ability fall short of its wishes. To address the problem, we propose a new non end-to-end training strategy and explore different designs of multi-stage architecture for surgical phase recognition task. For the non end-to-end training strategy, the refinement stage is trained separately with proposed two types of disturbed sequences. Meanwhile, we evaluate three different choices of refinement models to show that our analysis and solution are robust to the choices of specific multi-stage models. We conduct experiments on two public benchmarks, the M2CAI16 Workflow Challenge, and the Cholec80 dataset. Results show that multi-stage architecture trained with our strategy largely boosts the performance of the current state-of-the-art single-stage model. Code is available at \url{https://github.com/ChinaYi/casual_tcn}.
Speech2Video: Cross-Modal Distillation for Speech to Video Generation
Authors: Shijing Si, Jianzong Wang, Xiaoyang Qu, Ning Cheng, Wenqi Wei, Xinghua Zhu, Jing Xiao
Date: 2021-07-10 00:00:00
Tasks: Generative Adversarial Network, Video Generation

This paper investigates a novel task of talking face video generation solely from speeches. The speech-to-video generation technique can spark interesting applications in entertainment, customer service, and human-computer-interaction industries. Indeed, the timbre, accent and speed in speeches could contain rich information relevant to speakers' appearance. The challenge mainly lies in disentangling the distinct visual attributes from audio signals. In this article, we propose a light-weight, cross-modal distillation method to extract disentangled emotional and identity information from unlabelled video inputs. The extracted features are then integrated by a generative adversarial network into talking face video clips. With carefully crafted discriminators, the proposed framework achieves realistic generation results. Experiments with observed individuals demonstrated that the proposed framework captures the emotional expressions solely from speeches, and produces spontaneous facial motion in the video output. Compared to the baseline method where speeches are combined with a static image of the speaker, the results of the proposed framework is almost indistinguishable. User studies also show that the proposed method outperforms the existing algorithms in terms of emotion expression in the generated videos.
Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star
Authors: Bryar A. Hassan, Tarik A. Rashid, Seyedali Mirjalili
Date: 2021-07-10 00:00:00

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using FCA. However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is required to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising FCA; (2) A framework to decrease the formal contexts ambiguity of the first framework using an adaptive version of ECA*. Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
Authors: Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan, Joseph E. Gonzalez, Ken Goldberg
Date: 2021-07-10 00:00:00
Tasks: Binary Classification, Reinforcement Learning (RL)

Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained. A promising strategy for learning in dynamically uncertain environments is requiring that the agent can robustly return to learned safe sets, where task success (and therefore safety) can be guaranteed. While this approach has been successful in low-dimensions, enforcing this constraint in environments with visual observations is exceedingly challenging. We present a novel continuous representation for safe sets by framing it as a binary classification problem in a learned latent space, which flexibly scales to image observations. We then present a new algorithm, Latent Space Safe Sets (LS3), which uses this representation for long-horizon tasks with sparse rewards. We evaluate LS3 on 4 domains, including a challenging sequential pushing task in simulation and a physical cable routing task. We find that LS3 can use prior task successes to restrict exploration and learn more efficiently than prior algorithms while satisfying constraints. See https://tinyurl.com/latent-ss for code and supplementary material.
Similar Cases Recommendation using Legal Knowledge Graphs
Authors: Jaspreet Singh Dhani, Ruchika Bhatt, Balaji Ganesan, Parikshet Sirohi, Vasudha Bhatnagar
Date: 2021-07-10 00:00:00
Tasks: Knowledge Graphs, Question Answering

A legal knowledge graph constructed from court cases, judgments, laws and other legal documents can enable a number of applications like question answering, document similarity, and search. While the use of knowledge graphs for distant supervision in NLP tasks is well researched, using knowledge graphs for applications like case similarity presents challenges. In this work, we describe our solution for predicting similar cases in Indian court judgements. We present our results and also discuss the impact of large language models on this task.
DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering
Authors: Jianyu Wang, Bing-Kun Bao, Changsheng Xu
Date: 2021-07-10 00:00:00
Tasks: Graph Attention, Question Answering, Relational Reasoning, Video Question Answering, Visual Question Answering (VQA)

Video question answering is a challenging task, which requires agents to be able to understand rich video contents and perform spatial-temporal reasoning. However, existing graph-based methods fail to perform multi-step reasoning well, neglecting two properties of VideoQA: (1) Even for the same video, different questions may require different amount of video clips or objects to infer the answer with relational reasoning; (2) During reasoning, appearance and motion features have complicated interdependence which are correlated and complementary to each other. Based on these observations, we propose a Dual-Visual Graph Reasoning Unit (DualVGR) which reasons over videos in an end-to-end fashion. The first contribution of our DualVGR is the design of an explainable Query Punishment Module, which can filter out irrelevant visual features through multiple cycles of reasoning. The second contribution is the proposed Video-based Multi-view Graph Attention Network, which captures the relations between appearance and motion features. Our DualVGR network achieves state-of-the-art performance on the benchmark MSVD-QA and SVQA datasets, and demonstrates competitive results on benchmark MSRVTT-QA datasets. Our code is available at https://github.com/MMIR/DualVGR-VideoQA.
Anomaly Detection in Residential Video Surveillance on Edge Devices in IoT Framework
Authors: Mayur R. Parate, Kishor M. Bhurchandi, Ashwin G. Kothari
Date: 2021-07-10 00:00:00
Tasks: Anomaly Detection, CPU

Intelligent resident surveillance is one of the most essential smart community services. The increasing demand for security needs surveillance systems to be able to detect anomalies in surveillance scenes. Employing high-capacity computational devices for intelligent surveillance in residential societies is costly and not feasible. Therefore, we propose anomaly detection for intelligent surveillance using CPU-only edge devices. A modular framework to capture object-level inferences and tracking is developed. To cope with partial occlusions, posture deformations, and complex scenes, we employed feature encoding and trajectory association governed by two metrices complementing to each other. The elements of an anomaly detection framework are optimized to run on CPU-only edge devices with sufficient frames per second (FPS). The experimental results indicate the proposed method is feasible and achieves satisfactory results in real-life scenarios.