OpenRaiser/Intern-Atlas huggingface dataset:
TopicFM+: Boosting Accuracy and Efficiency of Topic-Assisted Feature Matching
Venue: arXiv cs.CV 2023
Year: 2023

This study tackles the challenge of image matching in difficult scenarios, such as scenes with significant variations or limited texture, with a strong emphasis on computational efficiency. Previous studies have attempted to address this challenge by encoding global scene contexts using Transformers. However, these approaches suffer from high computational costs and may not capture sufficient high-level contextual information, such as structural shapes or semantic instances. Consequently, the encoded features may lack discriminative power in challenging scenes. To overcome these limitations, we propose a novel image-matching method that leverages a topic-modeling strategy to capture high-level contexts in images. Our method represents each image as a multinomial distribution over topics, where each topic represents a latent semantic instance. By incorporating these topics, we can effectively capture comprehensive context information and obtain discriminative and high-quality features. Additionally, our method effectively matches features within corresponding semantic regions by estimating the covisible topics. To enhance the efficiency of feature matching, we have designed a network with a pooling-and-merging attention module. This module reduces computation by employing attention only on fixed-sized topics and small-sized features. Through extensive experiments, we have demonstrated the superiority of our method in challenging scenarios. Specifically, our method significantly reduces computational costs while maintaining higher image-matching accuracy compared to state-of-the-art methods. The code will be updated soon at https://github.com/TruongKhang/TopicFM
Cloud Ensemble Learning for Fault Diagnosis of Rolling Bearings with Stochastic Configuration Networks
Venue: arXiv cs.LG 2023
Year: 2023

Fault diagnosis of rolling bearings is of great significance for post-maintenance in rotating machinery, but it is a challenging work to diagnose faults efficiently with a few samples. Additionally, faults commonly occur with randomness and fuzziness due to the complexity of the external environment and the structure of rolling bearings, hindering effective mining of fault characteristics and eventually restricting accuracy of fault diagnosis. To overcome these problems, stochastic configuration network (SCN) based cloud ensemble learning, called SCN-CEL, is developed in this work. Concretely, a cloud feature extraction method is first developed by using a backward cloud generator of normal cloud model to mine the uncertainty of fault information. Then, a cloud sampling method, which generates enough cloud droplets using bidirectional cloud generator, is proposed to extend the cloud feature samples. Finally, an ensemble model with SCNs is developed to comprehensively characterize the uncertainty of fault information and advance the generalization performance of fault diagnosis machine. Experimental results demonstrate that the proposed method indeed performs favorably for distinguishing fault categories of rolling bearings in the few shot scenarios.
LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance
Venue: arXiv cs.CV 2023
Year: 2023

Recent large-scale text-guided diffusion models provide powerful image-generation capabilities. Currently, a significant effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing. However, editing proves to be difficult for these generative models due to the inherent nature of editing techniques, which involves preserving certain content from the original image. Conversely, in text-based models, even minor modifications to the text prompt frequently result in an entirely distinct result, making attaining one-shot generation that accurately corresponds to the users intent exceedingly challenging. In addition, to edit a real image using these state-of-the-art tools, one must first invert the image into the pre-trained models domain - adding another factor affecting the edit quality, as well as latency. In this exploratory report, we propose LEDITS - a combined lightweight approach for real-image editing, incorporating the Edit Friendly DDPM inversion technique with Semantic Guidance, thus extending Semantic Guidance to real image editing, while harnessing the editing capabilities of DDPM inversion as well. This approach achieves versatile edits, both subtle and extensive as well as alterations in composition and style, while requiring no optimization nor extensions to the architecture.
A MIL Approach for Anomaly Detection in Surveillance Videos from Multiple Camera Views
Venue: arXiv cs.CV 2023
Year: 2023

Occlusion and clutter are two scene states that make it difficult to detect anomalies in surveillance video. Furthermore, anomaly events are rare and, as a consequence, class imbalance and lack of labeled anomaly data are also key features of this task. Therefore, weakly supervised methods are heavily researched for this application. In this paper, we tackle these typical problems of anomaly detection in surveillance video by combining Multiple Instance Learning (MIL) to deal with the lack of labels and Multiple Camera Views (MC) to reduce occlusion and clutter effects. In the resulting MC-MIL algorithm we apply a multiple camera combined loss function to train a regression network with Sultani's MIL ranking function. To evaluate the MC-MIL algorithm first proposed here, the multiple camera PETS-2009 benchmark dataset was re-labeled for the anomaly detection task from multiple camera views. The result shows a significant performance improvement in F1 score compared to the single-camera configuration.
ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation
Venue: arXiv cs.SE 2023
Year: 2023

Recent advancements in large language models (LLMs) have demonstrated exceptional success in a wide range of general domain tasks, such as question answering and following instructions. Moreover, LLMs have shown potential in various software engineering applications. In this study, we present a systematic comparison of test suites generated by the ChatGPT LLM and the state-of-the-art SBST tool EvoSuite. Our comparison is based on several critical factors, including correctness, readability, code coverage, and bug detection capability. By highlighting the strengths and weaknesses of LLMs (specifically ChatGPT) in generating unit test cases compared to EvoSuite, this work provides valuable insights into the performance of LLMs in solving software engineering problems. Overall, our findings underscore the potential of LLMs in software engineering and pave the way for further research in this area.
MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval
Venue: arXiv cs.IR 2023
Year: 2023

Information retrieval (IR) is essential in biomedical knowledge acquisition and clinical decision support. While recent progress has shown that language model encoders perform better semantic retrieval, training such models requires abundant query-article annotations that are difficult to obtain in biomedicine. As a result, most biomedical IR systems only conduct lexical matching. In response, we introduce MedCPT, a first-of-its-kind Contrastively Pre-trained Transformer model for zero-shot semantic IR in biomedicine. To train MedCPT, we collected an unprecedented scale of 255 million user click logs from PubMed. With such data, we use contrastive learning to train a pair of closely-integrated retriever and re-ranker. Experimental results show that MedCPT sets new state-of-the-art performance on six biomedical IR tasks, outperforming various baselines including much larger models such as GPT-3-sized cpt-text-XL. In addition, MedCPT also generates better biomedical article and sentence representations for semantic evaluations. As such, MedCPT can be readily applied to various real-world biomedical IR tasks.
Numerical Association Rule Mining: A Systematic Literature Review
Venue: arXiv cs.LG 2023
Year: 2023

Numerical association rule mining is a widely used variant of the association rule mining technique, and it has been extensively used in discovering patterns and relationships in numerical data. Initially, researchers and scientists integrated numerical attributes in association rule mining using various discretization approaches; however, over time, a plethora of alternative methods have emerged in this field. Unfortunately, the increase of alternative methods has resulted into a significant knowledge gap in understanding diverse techniques employed in numerical association rule mining -- this paper attempts to bridge this knowledge gap by conducting a comprehensive systematic literature review. We provide an in-depth study of diverse methods, algorithms, metrics, and datasets derived from 1,140 scholarly articles published from the inception of numerical association rule mining in the year 1996 to 2022. In compliance with the inclusion, exclusion, and quality evaluation criteria, 68 papers were chosen to be extensively evaluated. To the best of our knowledge, this systematic literature review is the first of its kind to provide an exhaustive analysis of the current literature and previous surveys on numerical association rule mining. The paper discusses important research issues, the current status, and future possibilities of numerical association rule mining. On the basis of this systematic review, the article also presents a novel discretization measure that contributes by providing a partitioning of numerical data that meets well human perception of partitions.
CNN-BiLSTM model for English Handwriting Recognition: Comprehensive Evaluation on the IAM Dataset
Venue: arXiv cs.CV 2023
Year: 2023

We present a CNN-BiLSTM system for the problem of offline English handwriting recognition, with extensive evaluations on the public IAM dataset, including the effects of model size, data augmentation and the lexicon. Our best model achieves 3.59\% CER and 9.44\% WER using CNN-BiLSTM network with CTC layer. Test time augmentation with rotation and shear transformations applied to the input image, is proposed to increase recognition of difficult cases and found to reduce the word error rate by 2.5\% points. We also conduct an error analysis of our proposed method on IAM dataset, show hard cases of handwriting images and explore samples with erroneous labels. We provide our source code as public-domain, to foster further research to encourage scientific reproducibility.
Neural network execution using nicked DNA and microfluidics
Venue: arXiv cs.ET 2023
Year: 2023

DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, computing on data stored in DNA is a largely unexplored challenge. This paper proposes an integrated circuit (IC) based on microfluidics that can perform complex operations such as artificial neural network (ANN) computation on data stored in DNA. It computes entirely in the molecular domain without converting data to electrical form, making it a form of in-memory computing on DNA. The computation is achieved by topologically modifying DNA strands through the use of enzymes called nickases. A novel scheme is proposed for representing data stochastically through the concentration of the DNA molecules that are nicked at specific sites. The paper provides details of the biochemical design, as well as the design, layout, and operation of the microfluidics device. Benchmarks are reported on the performance of neural network computation.
ROAR: Robust Adaptive Reconstruction of Shapes Using Planar Projections
Venue: arXiv cs.GR 2023
Year: 2023

The majority of existing large 3D shape datasets contain meshes that lend themselves extremely well to visual applications such as rendering, yet tend to be topologically invalid (i.e, contain non-manifold edges and vertices, disconnected components, self-intersections). Therefore, it is of no surprise that state of the art studies in shape understanding do not explicitly use this 3D information. In conjunction with this, triangular meshes remain the dominant shape representation for many downstream tasks, and their connectivity remain a relatively untapped source of potential for more profound shape reasoning. In this paper, we introduce ROAR, an iterative geometry/topology evolution approach to reconstruct 2-manifold triangular meshes from arbitrary 3D shape representations, that is highly suitable for large existing in-the-wild datasets. ROAR leverages the visual prior large datasets exhibit by evolving the geometry of the mesh via a 2D render loss, and a novel 3D projection loss, the Planar Projection. After each geometry iteration, our system performs topological corrections. Self-intersections are reduced following a geometrically motivated attenuation term, and resolution is added to required regions using a novel face scoring function. These steps alternate until convergence is achieved, yielding a high-quality manifold mesh. We evaluate ROAR on the notoriously messy yet popular dataset ShapeNet, and present ShapeROAR - a topologically valid yet still geometrically accurate version of ShapeNet. We compare our results to state-of-the-art reconstruction methods and demonstrate superior shape faithfulness, topological correctness, and triangulation quality. In addition, we demonstrate reconstructing a mesh from neural Signed Distance Functions (SDF), and achieve comparable Chamfer distance with much fewer SDF sampling operations than the commonly used Marching Cubes approach.
Exploring the Advances in Identifying Useful Code Review Comments
Venue: arXiv cs.SE 2023
Year: 2023

Effective peer code review in collaborative software development necessitates useful reviewer comments and supportive automated tools. Code review comments are a central component of the Modern Code Review process in the industry and open-source development. Therefore, it is important to ensure these comments serve their purposes. This paper reflects the evolution of research on the usefulness of code review comments. It examines papers that define the usefulness of code review comments, mine and annotate datasets, study developers' perceptions, analyze factors from different aspects, and use machine learning classifiers to automatically predict the usefulness of code review comments. Finally, it discusses the open problems and challenges in recognizing useful code review comments for future research.
Graph-level Anomaly Detection via Hierarchical Memory Networks
Venue: arXiv cs.LG 2023
Year: 2023

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules -- node and graph memory modules -- via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs. Extensive empirical results on 16 real-world graph datasets from various domains show that i) HimNet significantly outperforms the state-of-art methods and ii) it is robust to anomaly contamination. Codes are available at: https://github.com/Niuchx/HimNet.
ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution
Venue: arXiv cs.CV 2023
Year: 2023

Diffusion models have gained significant popularity in the field of image-to-image translation. Previous efforts applying diffusion models to image super-resolution (SR) have demonstrated that iteratively refining pure Gaussian noise using a U-Net architecture trained on denoising at various noise levels can yield satisfactory high-resolution images from low-resolution inputs. However, this iterative refinement process comes with the drawback of low inference speed, which strongly limits its applications. To speed up inference and further enhance the performance, our research revisits diffusion models in image super-resolution and proposes a straightforward yet significant diffusion model-based super-resolution method called ACDMSR (accelerated conditional diffusion model for image super-resolution). Specifically, our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our study also highlights the effectiveness of using a pre-trained SR model to provide the conditional image of the given low-resolution (LR) image to achieve superior high-resolution results. We demonstrate that our method surpasses previous attempts in qualitative and quantitative results through extensive experiments conducted on benchmark datasets such as Set5, Set14, Urban100, BSD100, and Manga109. Moreover, our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
Evaluating Shutdown Avoidance of Language Models in Textual Scenarios
Venue: arXiv cs.CL 2023
Year: 2023

Recently, there has been an increase in interest in evaluating large language models for emergent and dangerous capabilities. Importantly, agents could reason that in some scenarios their goal is better achieved if they are not turned off, which can lead to undesirable behaviors. In this paper, we investigate the potential of using toy textual scenarios to evaluate instrumental reasoning and shutdown avoidance in language models such as GPT-4 and Claude. Furthermore, we explore whether shutdown avoidance is merely a result of simple pattern matching between the dataset and the prompt or if it is a consistent behaviour across different environments and variations. We evaluated behaviours manually and also experimented with using language models for automatic evaluations, and these evaluations demonstrate that simple pattern matching is likely not the sole contributing factor for shutdown avoidance. This study provides insights into the behaviour of language models in shutdown avoidance scenarios and inspires further research on the use of textual scenarios for evaluations.
SketchMetaFace: A Learning-based Sketching Interface for High-fidelity 3D Character Face Modeling
Venue: arXiv cs.CV 2023
Year: 2023

Modeling 3D avatars benefits various application scenarios such as AR/VR, gaming, and filming. Character faces contribute significant diversity and vividity as a vital component of avatars. However, building 3D character face models usually requires a heavy workload with commercial tools, even for experienced artists. Various existing sketch-based tools fail to support amateurs in modeling diverse facial shapes and rich geometric details. In this paper, we present SketchMetaFace - a sketching system targeting amateur users to model high-fidelity 3D faces in minutes. We carefully design both the user interface and the underlying algorithm. First, curvature-aware strokes are adopted to better support the controllability of carving facial details. Second, considering the key problem of mapping a 2D sketch map to a 3D model, we develop a novel learning-based method termed "Implicit and Depth Guided Mesh Modeling" (IDGMM). It fuses the advantages of mesh, implicit, and depth representations to achieve high-quality results with high efficiency. In addition, to further support usability, we present a coarse-to-fine 2D sketching interface design and a data-driven stroke suggestion tool. User studies demonstrate the superiority of our system over existing modeling tools in terms of the ease to use and visual quality of results. Experimental analyses also show that IDGMM reaches a better trade-off between accuracy and efficiency. SketchMetaFace is available at https://zhongjinluo.github.io/SketchMetaFace/.
Short Flip Sequences to Untangle Segments in the Plane
Venue: arXiv cs.CG 2023
Year: 2023

A (multi)set of segments in the plane may form a TSP tour, a matching, a tree, or any multigraph. If two segments cross, then we can reduce the total length with the following flip operation. We remove a pair of crossing segments, and insert a pair of non-crossing segments, while keeping the same vertex degrees. The goal of this paper is to devise efficient strategies to flip the segments in order to obtain crossing-free segments after a small number of flips. Linear and near-linear bounds on the number of flips were only known for segments with endpoints in convex position. We generalize these results, proving linear and near-linear bounds for cases with endpoints that are not in convex position. Our results are proved in a general setting that applies to multiple problems, using multigraphs and the distinction between removal and insertion choices when performing a flip.
Review of Large Vision Models and Visual Prompt Engineering
Venue: arXiv cs.CV 2023
Year: 2023

Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilities. As the development of large vision models progresses, the importance of prompt engineering becomes increasingly evident. Designing suitable prompts for specific visual tasks has emerged as a meaningful research direction. This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering. We present influential large models in the visual domain and a range of prompt engineering methods employed on these models. It is our hope that this review provides a comprehensive and systematic description of prompt engineering methods based on large visual models, offering valuable insights for future researchers in their exploration of this field.
Many tasks make light work: Learning to localise medical anomalies from multiple synthetic tasks
Venue: arXiv cs.CV 2023
Year: 2023

There is a growing interest in single-class modelling and out-of-distribution detection as fully supervised machine learning models cannot reliably identify classes not included in their training. The long tail of infinitely many out-of-distribution classes in real-world scenarios, e.g., for screening, triage, and quality control, means that it is often necessary to train single-class models that represent an expected feature distribution, e.g., from only strictly healthy volunteer data. Conventional supervised machine learning would require the collection of datasets that contain enough samples of all possible diseases in every imaging modality, which is not realistic. Self-supervised learning methods with synthetic anomalies are currently amongst the most promising approaches, alongside generative auto-encoders that analyse the residual reconstruction error. However, all methods suffer from a lack of structured validation, which makes calibration for deployment difficult and dataset-dependant. Our method alleviates this by making use of multiple visually-distinct synthetic anomaly learning tasks for both training and validation. This enables more robust training and generalisation. With our approach we can readily outperform state-of-the-art methods, which we demonstrate on exemplars in brain MRI and chest X-rays. Code is available at https://github.com/matt-baugh/many-tasks-make-light-work .