pwc-archive/papers-with-abstracts huggingface dataset:
Noisy Channel for Automatic Text Simplification
Authors: Oscar M Cumbicus-Pineda, Iker Gutiérrez-Fandiño, Itziar Gonzalez-Dios, Aitor Soroa
Date: 2022-11-06 00:00:00
Tasks: Language Modeling, Language Modelling, Re-Ranking, Sentence, Text Simplification

In this paper we present a simple re-ranking method for Automatic Sentence Simplification based on the noisy channel scheme. Instead of directly computing the best simplification given a complex text, the re-ranking method also considers the probability of the simple sentence to produce the complex counterpart, as well as the probability of the simple text itself, according to a language model. Our experiments show that combining these scores outperform the original system in three different English datasets, yielding the best known result in one of them. Adopting the noisy channel scheme opens new ways to infuse additional information into ATS systems, and thus to control important aspects of them, a known limitation of end-to-end neural seq2seq generative models.
MalGrid: Visualization Of Binary Features In Large Malware Corpora
Authors: Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath
Date: 2022-11-04 00:00:00

The number of malware is constantly on the rise. Though most new malware are modifications of existing ones, their sheer number is quite overwhelming. In this paper, we present a novel system to visualize and map millions of malware to points in a 2-dimensional (2D) spatial grid. This enables visualizing relationships within large malware datasets that can be used to develop triage solutions to screen different malware rapidly and provide situational awareness. Our approach links two visualizations within an interactive display. Our first view is a spatial point-based visualization of similarity among the samples based on a reduced dimensional projection of binary feature representations of malware. Our second spatial grid-based view provides a better insight into similarities and differences between selected malware samples in terms of the binary-based visual representations they share. We also provide a case study where the effect of packing on the malware data is correlated with the complexity of the packing algorithm.
PASTA: Table-Operations Aware Fact Verification via Sentence-Table Cloze Pre-training
Authors: Zihui Gu, Ju Fan, Nan Tang, Preslav Nakov, Xiaoman Zhao, Xiaoyong Du
Date: 2022-11-05 00:00:00
Tasks: Fact Checking, Fact Verification, Language Modelling, Marketing, Misinformation, Sentence, Table-based Fact Verification

Fact verification has attracted a lot of research attention recently, e.g., in journalism, marketing, and policymaking, as misinformation and disinformation online can sway one's opinion and affect one's actions. While fact-checking is a hard task in general, in many cases, false statements can be easily debunked based on analytics over tables with reliable information. Hence, table-based fact verification has recently emerged as an important and growing research area. Yet, progress has been limited due to the lack of datasets that can be used to pre-train language models (LMs) to be aware of common table operations, such as aggregating a column or comparing tuples. To bridge this gap, in this paper we introduce PASTA, a novel state-of-the-art framework for table-based fact verification via pre-training with synthesized sentence-table cloze questions. In particular, we design six types of common sentence-table cloze tasks, including Filter, Aggregation, Superlative, Comparative, Ordinal, and Unique, based on which we synthesize a large corpus consisting of 1.2 million sentence-table pairs from WikiTables. PASTA uses a recent pre-trained LM, DeBERTaV3, and further pretrains it on our corpus. Our experimental results show that PASTA achieves new state-of-the-art performance on two table-based fact verification benchmarks: TabFact and SEM-TAB-FACTS. In particular, on the complex set of TabFact, which contains multiple operations, PASTA largely outperforms the previous state of the art by 4.7 points (85.6% vs. 80.9%), and the gap between PASTA and human performance on the small TabFact test set is narrowed to just 1.5 points (90.6% vs. 92.1%).
Group DETR v2: Strong Object Detector with Encoder-Decoder Pretraining
Authors: Qiang Chen, Jian Wang, Chuchu Han, Shan Zhang, Zexian Li, Xiaokang Chen, Jiahui Chen, Xiaodi Wang, Shuming Han, Gang Zhang, Haocheng Feng, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang
Date: 2022-11-07 00:00:00
Proceeding: arXiv 2022 11
Tasks: Decoder, Object, object-detection, Object Detection

We present a strong object detector with encoder-decoder pretraining and finetuning. Our method, called Group DETR v2, is built upon a vision transformer encoder ViT-Huge~\cite{dosovitskiy2020image}, a DETR variant DINO~\cite{zhang2022dino}, and an efficient DETR training method Group DETR~\cite{chen2022group}. The training process consists of self-supervised pretraining and finetuning a ViT-Huge encoder on ImageNet-1K, pretraining the detector on Object365, and finally finetuning it on COCO. Group DETR v2 achieves $\textbf{64.5}$ mAP on COCO test-dev, and establishes a new SoTA on the COCO leaderboard https://paperswithcode.com/sota/object-detection-on-coco
Micro Expression Generation with Thin-plate Spline Motion Model and Face Parsing
Authors: Jun Yu, Guochen Xie, Zhongpeng Cai, Peng He, Fang Gao, Qiang Ling
Date: 2022-10-10 00:00:00
Proceeding: MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 10
Tasks: Face Parsing, Micro-expression Generation, Motion Estimation, Optical Flow Estimation

Micro-expression generation aims at transfering the expression from the driving videos to the source images, which can be viewed as a motion transfer task. Recently, several works have been proposed to tackle this problem and achieve great performance. However, due to the intrinsic complexity of the face motion and different attributes of face regions, the task still remains challenging. In this paper, we propose an end-to-end unsupervised motion transfer network to tackle this challenge. As the motion of the face is non-rigid, we adopt an effective and flexible thin-plate spline motion estimation method to estimate the optical flow of the face motion. What's more, we find that several faces with eyeglasses show weird deformation in motion transfering. Thus, we introduce face parsing method to pay specific attention to the eyeglasses regions to ensure the reasonability of the deformation. We conduct several experiments on the provided datasets of the ACM MM 2022 micro-expression grand challenge (MEGC2022) and compare our method with several other typical methods. In comparison, our method shows the best performance. We (Team: USTC-IAT-United) also compare our method with other competitors' in MEGC2022, and the expert evaluation results show that our method performs best, which verifies the effectiveness of our method. Our code is available at https://github.com/HowToNameMe/micro-expression
Facial Expression Spotting Based on Optical Flow Features
Authors: Jun Yu, Zhongpeng Cai, Zepeng Liu, Guochen Xie, Peng He
Date: 2022-10-10 00:00:00
Proceeding: MM '22: Proceedings of the 30th ACM International Conference on Multimedia 2022 10
Tasks: Optical Flow Estimation

The purpose of micro expression (ME) and macro expression (MaE) spotting task is to locate the onset and offset frames of MaE and ME clips. Compared with MaEs, MEs are shorter in duration and lower in intensity, which makes MEs harder to be spotted. In this paper, we propose an efficient pipeline based on optical flow features to spot MEs and MaEs. We crop and align the faces and select the eyebrows area, nose area, and mouth area as our regions of interest to exclude the interference of extraneous factors on the face expression representation. Then, we extract the optical flow in these regions and enhance the features of the expressions in the optical flow with low-pass filter and EMD method. Finally, the sliding window method is used to locate the peaks of optical flow features and get the intervals containing MEs or MaEs. We evaluate the performance of our method on the MEGC2022-TestSet including 10 long videos from SAMM and CAS(ME)3 and achieve the first place in the MEGC2022 Challenge. The results prove the effectiveness of our method.
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages
Authors: Mehedi Hasan Bijoy, Nahid Hossain, Salekul Islam, Swakkhar Shatabda
Date: 2022-11-07 00:00:00
Tasks: Bangla Spelling Error Correction, Denoising

Spelling error correction is the task of identifying and rectifying misspelled words in texts. It is a potential and active research topic in Natural Language Processing because of numerous applications in human language understanding. The phonetically or visually similar yet semantically distinct characters make it an arduous task in any language. Earlier efforts on spelling error correction in Bangla and resource-scarce Indic languages focused on rule-based, statistical, and machine learning-based methods which we found rather inefficient. In particular, machine learning-based approaches, which exhibit superior performance to rule-based and statistical methods, are ineffective as they correct each character regardless of its appropriateness. In this paper, we propose a novel detector-purificator-corrector framework, DPCSpell based on denoising transformers by addressing previous issues. In addition to that, we present a method for large-scale corpus creation from scratch which in turn resolves the resource limitation problem of any left-to-right scripted language. The empirical outcomes demonstrate the effectiveness of our approach, which outperforms previous state-of-the-art methods by attaining an exact match (EM) score of 94.78%, a precision score of 0.9487, a recall score of 0.9478, an f1 score of 0.948, an f0.5 score of 0.9483, and a modified accuracy (MA) score of 95.16% for Bangla spelling error correction. The models and corpus are publicly available at https://tinyurl.com/DPCSpell.
Pseudo-Label Generation and Various Data Augmentation for Semi-Supervised Hyperspectral Object Detection
Authors: Jun Yu, Liwen Zhang, Shenshen Du, Hao Chang, Keda Lu, Zhong Zhang, Ye Yu, Lei Wang, Qiang Ling
Date: 2022-10-01 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2022 10
Tasks: Data Augmentation, object-detection, Object Detection, Pseudo Label, Semi-Supervised Object Detection

Semi-supervised learning is a highly researched problem, but existing semi-supervised object detection frameworks are based on RGB images, and existing pre-trained models cannot be used for hyperspectral images. To overcome these difficulties, this paper first select fewer but suitable data augmentation methods to improve the accuracy of the supervised model based on the labeled training set, which is suitable for the characteristics of hyperspectral images. Next, in order to make full use of the unlabeled training set, we generate pseudo-labels with the model trained in the first stage and mix the obtained pseudo-labels with the labeled training set. Then, a large number of strong data augmentation methods are added to make the final model better. We achieve the SOTA, with an AP of 26.35, on the Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC) in the CVPR 2022 Perception Beyond the Visible Spectrum Workshop, and win the first place in this Challenge.
Bag of Tricks and a Strong Baseline for FGVC
Authors: Jun Yu, Hao Chang, Keda Lu, Guochen Xie, Liwen Zhang, Zhongpeng Cai, Shenshen Du, Zhihong Wei, Zepeng Liu, Fang Gao, Feng Shuang
Date: 2022-09-05 00:00:00
Proceeding: Conference and Labs of the Evaluation Forum 2022 9
Tasks: Classification, Fine-Grained Image Classification

Fine-grained visual classification (FGVC), as a subclass classification task under the superclass, brings
more challenges. However, in addition to fine-grained features, the FungiCLEF 2022 dataset is also
characterized by imbalance and rich meta-information. This motivates us to explore the impact of different
methods and components in fine-grained classification on FungiCLEF 2022. We explore the impact of
different data augmentations, backbones, loss functions, and attention mechanisms on classification
performance. Additionally, we explore different metadata usage scenarios. In the end, we win second
place in the CVPR2022 FGVC Workshop FungiCLEF 2022 challenge. Our code is available at https:
//github.com/wujiekd/Bag-of-Tricks-and-a-Strong-Baseline-for-Fungi-Fine-Grained-Classification.
Mining limited data for more robust and generalized ML models
Authors: Jun Yu, Hao Chang, Keda Lu
Date: 2021-12-01 00:00:00
Proceeding: Association for the Advancement of Artificial Intelligence 2021 12

For the last ten years, the success of deep learning comes from big data and large models. In recent years, the architecture of neural networks has been very mature. It’s more efficient to look for ways improving the data based a fixed neural network architecture. Similarly, in terms of robust machine learning, defense methods based on deep learning models have been proposed to mitigate potential threats about adversarial samples, but most of them pursue high-performance models under fixed constraints and dataset. Therefore, how to construct universal and effective dataset to train robust models is still a problem to be explored. In this paper, we consider to generate a more robust and efficient machine learning model by mining limited data. In detail, we proposed Robust TrivialAugment(RTA) and Iterative Search to obtain better dataset to make the trained model achieve better performance with the same amount of data. Moreover, we have applied the proposed methods to competition AAAI2022 DataCentric Robust Learning on ML Models that is organized by Alibaba on the Tianchi platform and won top 10 in 3691 teams. Code is available at https://github.com/wujiekd/RTAIterative-Search-AAAI2022.
A viable framework for semi-supervised learning on realistic dataset
Authors: Hao Chang, Guochen Xie, Jun Yu, Qiang Ling, Fang Gao, Ye Yu
Date: 2022-07-21 00:00:00
Proceeding: Machine Learning 2022 7

Semi-supervised Fine-Grained Recognition is a challenging task due to the difficulty of data imbalance, high inter-class similarity and domain mismatch. Recently, this field has witnessed giant leap and many methods have gained great performance. We discover that these existing Semi-supervised Learning (SSL) methods achieve satisfactory performance owe to the exploration of unlabeled data. However, on the realistic large-scale datasets, due to the abovementioned challenges, the improvement of the quality of pseudo-labels requires further research. In this work, we propose Bilateral-Branch Self-Training Framework (BiSTF), a simple yet effective framework to improve existing semi-supervised learning methods on class-imbalanced and domain-shifted fine-grained data. By adjusting stochastic epoch update frequency, BiSTF iteratively retrains a baseline SSL model with a labeled set expanded by selectively adding pseudo-labeled samples from an unlabeled set, where the distribution of pseudo-labeled samples is the same as the labeled data. We show that BiSTF outperforms the existing state-of-the-art SSL algorithm on Semi-iNat dataset. Our code is available at https://github.com/HowieChangchn/BiSTF.
SLOPT: Bandit Optimization Framework for Mutation-Based Fuzzing
Authors: Yuki Koike, Hiroyuki Katsura, Hiromu Yakura, Yuma Kurogome
Date: 2022-11-07 00:00:00
Tasks: CPU

Mutation-based fuzzing has become one of the most common vulnerability discovery solutions over the last decade. Fuzzing can be optimized when targeting specific programs, and given that, some studies have employed online optimization methods to do it automatically, i.e., tuning fuzzers for any given program in a program-agnostic manner. However, previous studies have neither fully explored mutation schemes suitable for online optimization methods, nor online optimization methods suitable for mutation schemes. In this study, we propose an optimization framework called SLOPT that encompasses both a bandit-friendly mutation scheme and mutation-scheme-friendly bandit algorithms. The advantage of SLOPT is that it can generally be incorporated into existing fuzzers, such as AFL and Honggfuzz. As a proof of concept, we implemented SLOPT-AFL++ by integrating SLOPT into AFL++ and showed that the program-agnostic optimization delivered by SLOPT enabled SLOPT-AFL++ to achieve higher code coverage than AFL++ in all of ten real-world FuzzBench programs. Moreover, we ran SLOPT-AFL++ against several real-world programs from OSS-Fuzz and successfully identified three previously unknown vulnerabilities, even though these programs have been fuzzed by AFL++ for a considerable number of CPU days on OSS-Fuzz.
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X Orchestration
Authors: Haolin Zhang, M S Mekala, Zulkar Nain, Dongfang Yang, Ju H. Park, Ho-Youl Jung
Date: 2022-11-07 00:00:00
Tasks: 3D Object Detection, Edge-computing, object-detection, Object Detection

Edge computing-based 3D perception has received attention in intelligent transportation systems (ITS) because real-time monitoring of traffic candidates potentially strengthens Vehicle-to-Everything (V2X) orchestration. Thanks to the capability of precisely measuring the depth information on surroundings from LiDAR, the increasing studies focus on lidar-based 3D detection, which significantly promotes the development of 3D perception. Few methods met the real-time requirement of edge deployment because of high computation-intensive operations. Moreover, an inconsistency problem of object detection remains uncovered in the pointcloud domain due to large sparsity. This paper thoroughly analyses this problem, comprehensively roused by recent works on determining inconsistency problems in the image specialisation. Therefore, we proposed a 3D harmonic loss function to relieve the pointcloud based inconsistent predictions. Moreover, the feasibility of 3D harmonic loss is demonstrated from a mathematical optimization perspective. The KITTI dataset and DAIR-V2X-I dataset are used for simulations, and our proposed method considerably improves the performance than benchmark models. Further, the simulative deployment on an edge device (Jetson Xavier TX) validates our proposed model's efficiency.
Learning Visual Locomotion with Cross-Modal Supervision
Authors: Antonio Loquercio, Ashish Kumar, Jitendra Malik
Date: 2022-11-07 00:00:00

In this work, we show how to learn a visual walking policy that only uses a monocular RGB camera and proprioception. Since simulating RGB is hard, we necessarily have to learn vision in the real world. We start with a blind walking policy trained in simulation. This policy can traverse some terrains in the real world but often struggles since it lacks knowledge of the upcoming geometry. This can be resolved with the use of vision. We train a visual module in the real world to predict the upcoming terrain with our proposed algorithm Cross-Modal Supervision (CMS). CMS uses time-shifted proprioception to supervise vision and allows the policy to continually improve with more real-world experience. We evaluate our vision-based walking policy over a diverse set of terrains including stairs (up to 19cm high), slippery slopes (inclination of 35 degrees), curbs and tall steps (up to 20cm), and complex discrete terrains. We achieve this performance with less than 30 minutes of real-world data. Finally, we show that our policy can adapt to shifts in the visual field with a limited amount of real-world experience. Video results and code at https://antonilo.github.io/vision_locomotion/.
Issues and Challenges in Applications of Artificial Intelligence to Nuclear Medicine -- The Bethesda Report (AI Summit 2022)
Authors: Arman Rahmim, Tyler J. Bradshaw, Irène Buvat, Joyita Dutta, Abhinav K. Jha, Paul E. Kinahan, Quanzheng Li, Chi Liu, Melissa D. McCradden, Babak Saboury, Eliot Siegel, John J. Sunderland, Richard L. Wahl
Date: 2022-11-07 00:00:00

The SNMMI Artificial Intelligence (SNMMI-AI) Summit, organized by the SNMMI AI Task Force, took place in Bethesda, MD on March 21-22, 2022. It brought together various community members and stakeholders from academia, healthcare, industry, patient representatives, and government (NIH, FDA), and considered various key themes to envision and facilitate a bright future for routine, trustworthy use of AI in nuclear medicine. In what follows, essential issues, challenges, controversies and findings emphasized in the meeting are summarized.
On minimal variations for unsupervised representation learning
Authors: Vivien Cabannes, Alberto Bietti, Randall Balestriero
Date: 2022-11-07 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Unsupervised representation learning aims at describing raw data efficiently to solve various downstream tasks. It has been approached with many techniques, such as manifold learning, diffusion maps, or more recently self-supervised learning. Those techniques are arguably all based on the underlying assumption that target functions, associated with future downstream tasks, have low variations in densely populated regions of the input space. Unveiling minimal variations as a guiding principle behind unsupervised representation learning paves the way to better practical guidelines for self-supervised learning algorithms.
Are AlphaZero-like Agents Robust to Adversarial Perturbations?
Authors: Li-Cheng Lan, huan zhang, Ti-Rong Wu, Meng-Yu Tsai, I-Chen Wu, Cho-Jui Hsieh
Date: 2022-11-07 00:00:00
Tasks: Adversarial Attack, Board Games, Game of Go

The success of AlphaZero (AZ) has demonstrated that neural-network-based Go AIs can surpass human performance by a large margin. Given that the state space of Go is extremely large and a human player can play the game from any legal state, we ask whether adversarial states exist for Go AIs that may lead them to play surprisingly wrong actions. In this paper, we first extend the concept of adversarial examples to the game of Go: we generate perturbed states that are ``semantically'' equivalent to the original state by adding meaningless moves to the game, and an adversarial state is a perturbed state leading to an undoubtedly inferior action that is obvious even for Go beginners. However, searching the adversarial state is challenging due to the large, discrete, and non-differentiable search space. To tackle this challenge, we develop the first adversarial attack on Go AIs that can efficiently search for adversarial states by strategically reducing the search space. This method can also be extended to other board games such as NoGo. Experimentally, we show that the actions taken by both Policy-Value neural network (PV-NN) and Monte Carlo tree search (MCTS) can be misled by adding one or two meaningless stones; for example, on 58\% of the AlphaGo Zero self-play games, our method can make the widely used KataGo agent with 50 simulations of MCTS plays a losing action by adding two meaningless stones. We additionally evaluated the adversarial examples found by our algorithm with amateur human Go players and 90\% of examples indeed lead the Go agent to play an obviously inferior action. Our code is available at \url{https://PaperCode.cc/GoAttack}.