pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Camouflaged Object Detection and Beyond
Authors: Fengyang Xiao, Sujie Hu, Yuqi Shen, Chengyu Fang, Jinfa Huang, Chunming He, Longxiang Tang, Ziyun Yang, Xiu Li
Date: 2024-08-26 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Semantic Segmentation, Survey

Camouflaged Object Detection (COD) refers to the task of identifying and segmenting objects that blend seamlessly into their surroundings, posing a significant challenge for computer vision systems. In recent years, COD has garnered widespread attention due to its potential applications in surveillance, wildlife conservation, autonomous systems, and more. While several surveys on COD exist, they often have limitations in terms of the number and scope of papers covered, particularly regarding the rapid advancements made in the field since mid-2023. To address this void, we present the most comprehensive review of COD to date, encompassing both theoretical frameworks and practical contributions to the field. This paper explores various COD methods across four domains, including both image-level and video-level solutions, from the perspectives of traditional and deep learning approaches. We thoroughly investigate the correlations between COD and other camouflaged scenario methods, thereby laying the theoretical foundation for subsequent analyses. Beyond object-level detection, we also summarize extended methods for instance-level tasks, including camouflaged instance segmentation, counting, and ranking. Additionally, we provide an overview of commonly used benchmarks and evaluation metrics in COD tasks, conducting a comprehensive evaluation of deep learning-based techniques in both image and video domains, considering both qualitative and quantitative performance. Finally, we discuss the limitations of current COD models and propose 9 promising directions for future research, focusing on addressing inherent challenges and exploring novel, meaningful technologies. For those interested, a curated list of COD-related techniques, datasets, and additional resources can be found at https://github.com/ChunmingHe/awesome-concealed-object-segmentation
Revisiting Image Captioning Training Paradigm via Direct CLIP-based Optimization
Authors: Nicholas Moratelli, Davide Caffagni, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
Date: 2024-08-26 00:00:00
Tasks: Descriptive, Image Captioning

The conventional training approach for image captioning involves pre-training a network using teacher forcing and subsequent fine-tuning with Self-Critical Sequence Training to maximize hand-crafted captioning metrics. However, when attempting to optimize modern and higher-quality metrics like CLIP-Score and PAC-Score, this training method often encounters instability and fails to acquire the genuine descriptive capabilities needed to produce fluent and informative captions. In this paper, we propose a new training paradigm termed Direct CLIP-Based Optimization (DiCO). Our approach jointly learns and optimizes a reward model that is distilled from a learnable captioning evaluator with high human correlation. This is done by solving a weighted classification problem directly inside the captioner. At the same time, DiCO prevents divergence from the original model, ensuring that fluency is maintained. DiCO not only exhibits improved stability and enhanced quality in the generated captions but also aligns more closely with human preferences compared to existing methods, especially in modern metrics. Additionally, it maintains competitive performance in traditional metrics. Our source code and trained models are publicly available at https://github.com/aimagelab/DiCO.
Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses
Authors: Vassilissa Lehoux-Lebacque, Tomi Silander, Christelle Loiodice, Seungjoon Lee, Albert Wang, Sofia Michel
Date: 2024-08-26 00:00:00
Tasks: Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is an important optimization problem underlying the deployment of robots in automated warehouses and factories. Despite the large body of work on this topic, most approaches make heavy simplifications, both on the environment and the agents, which make the resulting algorithms impractical for real-life scenarios. In this paper, we consider a realistic problem of online order delivery in a warehouse, where a fleet of robots bring the products belonging to each order from shelves to workstations. This creates a stream of inter-dependent pickup and delivery tasks and the associated MAPF problem consists of computing realistic collision-free robot trajectories fulfilling these tasks. To solve this MAPF problem, we propose an extension of the standard Prioritized Planning algorithm to deal with the inter-dependent tasks (Interleaved Prioritized Planning) and a novel Via-Point Star (VP*) algorithm to compute an optimal dynamics-compliant robot trajectory to visit a sequence of goal locations while avoiding moving obstacles. We prove the completeness of our approach and evaluate it in simulation as well as in a real warehouse.
Estimating Uncertainty with Implicit Quantile Network
Authors: Yi Hung Lim
Date: 2024-08-26 00:00:00
Tasks: Ensemble Learning, Uncertainty Quantification

Uncertainty quantification is an important part of many performance critical applications. This paper provides a simple alternative to existing approaches such as ensemble learning and bayesian neural networks. By directly modeling the loss distribution with an Implicit Quantile Network, we get an estimate of how uncertain the model is of its predictions. For experiments with MNIST and CIFAR datasets, the mean of the estimated loss distribution is 2x higher for incorrect predictions. When data with high estimated uncertainty is removed from the test dataset, the accuracy of the model goes up as much as 10%. This method is simple to implement while offering important information to applications where the user has to know when the model could be wrong (e.g. deep learning for healthcare).
Retrieval Augmented Generation for Dynamic Graph Modeling
Authors: Yuxia Wu, Yuan Fang, Lizi Liao
Date: 2024-08-26 00:00:00
Tasks: Contrastive Learning, Retrieval, Retrieval-augmented Generation

Dynamic graph modeling is crucial for analyzing evolving patterns in various applications. Existing approaches often integrate graph neural networks with temporal modules or redefine dynamic graph modeling as a generative sequence task. However, these methods typically rely on isolated historical contexts of the target nodes from a narrow perspective, neglecting occurrences of similar patterns or relevant cases associated with other nodes. In this work, we introduce the Retrieval-Augmented Generation for Dynamic Graph Modeling (RAG4DyG) framework, which leverages guidance from contextually and temporally analogous examples to broaden the perspective of each node. This approach presents two critical challenges: (1) How to identify and retrieve high-quality demonstrations that are contextually and temporally analogous to dynamic graph samples? (2) How can these demonstrations be effectively integrated to improve dynamic graph modeling? To address these challenges, we propose RAG4DyG, which enriches the understanding of historical contexts by retrieving and learning from contextually and temporally pertinent demonstrations. Specifically, we employ a time- and context-aware contrastive learning module to identify and retrieve relevant cases for each query sequence. Moreover, we design a graph fusion strategy to integrate the retrieved cases, thereby augmenting the inherent historical contexts for improved prediction. Extensive experiments on real-world datasets across different domains demonstrate the effectiveness of RAG4DyG for dynamic graph modeling.
Towards Graph Prompt Learning: A Survey and Beyond
Authors: Qingqing Long, Yuchen Yan, Peiyan Zhang, Chen Fang, Wentao Cui, Zhiyuan Ning, Meng Xiao, Ning Cao, Xiao Luo, Lingjun Xu, Shiyue Jiang, Zheng Fang, Chong Chen, Xian-Sheng Hua, Yuanchun Zhou
Date: 2024-08-26 00:00:00
Tasks: Graph Mining, Prompt Learning, Question Answering, Recommendation Systems, Survey

Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval. This approach fully leverages the potential of large-scale pre-trained models, reducing downstream data requirements and computational costs while enhancing model applicability across various tasks. Graphs, as versatile data structures that capture relationships between entities, play pivotal roles in fields such as social network analysis, recommender systems, and biological graphs. Despite the success of pre-train and prompt learning paradigms in Natural Language Processing (NLP) and Computer Vision (CV), their application in graph domains remains nascent. In graph-structured data, not only do the node and edge features often have disparate distributions, but the topological structures also differ significantly. This diversity in graph data can lead to incompatible patterns or gaps between pre-training and fine-tuning on downstream graphs. We aim to bridge this gap by summarizing methods for alleviating these disparities. This includes exploring prompt design methodologies, comparing related techniques, assessing application scenarios and datasets, and identifying unresolved problems and challenges. This survey categorizes over 100 relevant works in this field, summarizing general design principles and the latest applications, including text-attributed graphs, molecules, proteins, and recommendation systems. Through this extensive review, we provide a foundational understanding of graph prompt learning, aiming to impact not only the graph mining community but also the broader Artificial General Intelligence (AGI) community.
A Joint Learning Model with Variational Interaction for Multilingual Program Translation
Authors: Yali Du, Hui Sun, Ming Li
Date: 2024-08-25 00:00:00
Tasks: Disentanglement, Translation, Variational Inference

Programs implemented in various programming languages form the foundation of software applications. To alleviate the burden of program migration and facilitate the development of software systems, automated program translation across languages has garnered significant attention. Previous approaches primarily focus on pairwise translation paradigms, learning translation between pairs of languages using bilingual parallel data. However, parallel data is difficult to collect for some language pairs, and the distribution of program semantics across languages can shift, posing challenges for pairwise program translation. In this paper, we argue that jointly learning a unified model to translate code across multiple programming languages is superior to separately learning from bilingual parallel data. We propose Variational Interaction for Multilingual Program Translation~(VIM-PT), a disentanglement-based generative approach that jointly trains a unified model for multilingual program translation across multiple languages. VIM-PT disentangles code into language-shared and language-specific features, using variational inference and interaction information with a novel lower bound, then achieves program translation through conditional generation. VIM-PT demonstrates four advantages: 1) captures language-shared information more accurately from various implementations and improves the quality of multilingual program translation, 2) mines and leverages the capability of non-parallel data, 3) addresses the distribution shift of program semantics across languages, 4) and serves as a unified model, reducing deployment complexity.
Variational autoencoder-based neural network model compression
Authors: Liang Cheng, Peiyuan Guan, Amir Taherkordi, Lei Liu, Dapeng Lan
Date: 2024-08-25 00:00:00
Tasks: Anomaly Detection, Image Generation, model, Model Compression, Quantization

Variational Autoencoders (VAEs), as a form of deep generative model, have been widely used in recent years, and shown great great peformance in a number of different domains, including image generation and anomaly detection, etc.. This paper aims to explore neural network model compression method based on VAE. The experiment uses different neural network models for MNIST recognition as compression targets, including Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). These models are the most basic models in deep learning, and other more complex and advanced models are based on them or inherit their features and evolve. In the experiment, the first step is to train the models mentioned above, each trained model will have different accuracy and number of total parameters. And then the variants of parameters for each model are processed as training data in VAEs separately, and the trained VAEs are tested by the true model parameters. The experimental results show that using the latent space as a representation of the model compression can improve the compression rate compared to some traditional methods such as pruning and quantization, meanwhile the accuracy is not greatly affected using the model parameters reconstructed based on the latent space. In the future, a variety of different large-scale deep learning models will be used more widely, so exploring different ways to save time and space on saving or transferring models will become necessary, and the use of VAE in this paper can provide a basis for these further explorations.
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token Embeddings
Authors: Duo Wang, Yuan Zuo, FengZhi Li, Junjie Wu
Date: 2024-08-25 00:00:00
Tasks: Language Modelling, Link Prediction, Node Classification, Prompt Learning, Self-Supervised Learning, Zero-Shot Learning

Zero-shot graph machine learning, especially with graph neural networks (GNNs), has garnered significant interest due to the challenge of scarce labeled data. While methods like self-supervised learning and graph prompt learning have been extensively explored, they often rely on fine-tuning with task-specific labels, limiting their effectiveness in zero-shot scenarios. Inspired by the zero-shot capabilities of instruction-fine-tuned large language models (LLMs), we introduce a novel framework named Token Embedding-Aligned Graph Language Model (TEA-GLM) that leverages LLMs as cross-dataset and cross-task zero-shot learners for graph machine learning. Concretely, we pretrain a GNN, aligning its representations with token embeddings of an LLM. We then train a linear projector that transforms the GNN's representations into a fixed number of graph token embeddings without tuning the LLM. A unified instruction is designed for various graph tasks at different levels, such as node classification (node-level) and link prediction (edge-level). These design choices collectively enhance our method's effectiveness in zero-shot learning, setting it apart from existing methods. Experiments show that our graph token embeddings help the LLM predictor achieve state-of-the-art performance on unseen datasets and tasks compared to other methods using LLMs as predictors.
Artificial intelligence for science: The easy and hard problems
Authors: Ruairidh M. Battleday, Samuel J. Gershman
Date: 2024-08-24 00:00:00
Tasks: scientific discovery

A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence. These almost all result from training flexible algorithms to solve difficult optimization problems specified in advance by teams of domain scientists and engineers with access to large amounts of data. Although extremely useful, this kind of problem solving only corresponds to one part of science - the "easy problem." The other part of scientific research is coming up with the problem itself - the "hard problem." Solving the hard problem is beyond the capacities of current algorithms for scientific discovery because it requires continual conceptual revision based on poorly defined constraints. We can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms.
Prompt-Matcher: Leveraging Large Models to Reduce Uncertainty in Schema Matching Results
Authors: Longyu Feng, Huahang Li, Chen Jason Zhang
Date: 2024-08-24 00:00:00
Tasks: Computational Efficiency, Data Integration, Large Language Model, Management

Schema matching is the process of identifying correspondences between the elements of two given schemata, essential for database management systems, data integration, and data warehousing. For datasets across different scenarios, the optimal schema matching algorithm is different. For single algorithm, hyperparameter tuning also cases multiple results. All results assigned equal probabilities are stored in probabilistic databases to facilitate uncertainty management. The substantial degree of uncertainty diminishes the efficiency and reliability of data processing, thereby precluding the provision of more accurate information for decision-makers. To address this problem, we introduce a new approach based on fine-grained correspondence verification with specific prompt of Large Language Model. Our approach is an iterative loop that consists of three main components: (1) the correspondence selection algorithm, (2) correspondence verification, and (3) the update of probability distribution. The core idea is that correspondences intersect across multiple results, thereby linking the verification of correspondences to the reduction of uncertainty in candidate results. The task of selecting an optimal correspondence set to maximize the anticipated uncertainty reduction within a fixed budgetary framework is established as an NP-hard problem. We propose a novel $(1-1/e)$-approximation algorithm that significantly outperforms brute algorithm in terms of computational efficiency. To enhance correspondence verification, we have developed two prompt templates that enable GPT-4 to achieve state-of-the-art performance across two established benchmark datasets. Our comprehensive experimental evaluation demonstrates the superior effectiveness and robustness of the proposed approach.
Language Model Empowered Spatio-Temporal Forecasting via Physics-Aware Reprogramming
Authors: Hao Wang, Jindong Han, Wei Fan, Hao liu
Date: 2024-08-24 00:00:00
Tasks: energy management, Language Modeling, Language Modelling, Spatio-Temporal Forecasting, Time Series, Time Series Forecasting

Spatio-temporal forecasting is pivotal in numerous real-world applications, including transportation planning, energy management, and climate monitoring. In this work, we aim to harness the reasoning and generalization abilities of Pre-trained Language Models (PLMs) for more effective spatio-temporal forecasting, particularly in data-scarce scenarios. However, recent studies uncover that PLMs, which are primarily trained on textual data, often falter when tasked with modeling the intricate correlations in numerical time series, thereby limiting their effectiveness in comprehending spatio-temporal data. To bridge the gap, we propose RePST, a physics-aware PLM reprogramming framework tailored for spatio-temporal forecasting. Specifically, we first propose a physics-aware decomposer that adaptively disentangles spatially correlated time series into interpretable sub-components, which facilitates PLM to understand sophisticated spatio-temporal dynamics via a divide-and-conquer strategy. Moreover, we propose a selective discrete reprogramming scheme, which introduces an expanded spatio-temporal vocabulary space to project spatio-temporal series into discrete representations. This scheme minimizes the information loss during reprogramming and enriches the representations derived by PLMs. Extensive experiments on real-world datasets show that the proposed RePST outperforms twelve state-of-the-art baseline methods, particularly in data-scarce scenarios, highlighting the effectiveness and superior generalization capabilities of PLMs for spatio-temporal forecasting.
Is Functional Correctness Enough to Evaluate Code Language Models? Exploring Diversity of Generated Codes
Authors: Heejae Chon, Seonghyeon Lee, Jinyoung Yeo, Dongha Lee
Date: 2024-08-24 00:00:00
Tasks: Code Generation, Diversity

Language models (LMs) have exhibited impressive abilities in generating codes from natural language requirements. In this work, we highlight the diversity of code generated by LMs as a critical criterion for evaluating their code generation capabilities, in addition to functional correctness. Despite its practical implications, there is a lack of studies focused on assessing the diversity of generated code, which overlooks its importance in the development of code LMs. We propose a systematic approach to evaluate the diversity of generated code, utilizing various metrics for inter-code similarity as well as functional correctness. Specifically, we introduce a pairwise code similarity measure that leverages large LMs' capabilities in code understanding and reasoning, demonstrating the highest correlation with human judgment. We extensively investigate the impact of various factors on the quality of generated code, including model sizes, temperatures, training approaches, prompting strategies, and the difficulty of input problems. Our consistent observation of a positive correlation between the test pass score and the inter-code similarity score indicates that current LMs tend to produce functionally correct code with limited diversity.
Applying graph neural network to SupplyGraph for supply chain network
Authors: Kihwan Han
Date: 2024-08-23 00:00:00
Tasks: Graph Attention, Graph Neural Network

Supply chain networks describe interactions between products, manufacture facilities, storages in the context of supply and demand of the products. Supply chain data are inherently under graph structure; thus, it can be fertile ground for applications of graph neural network (GNN). Very recently, supply chain dataset, SupplyGraph, has been released to the public. Though the SupplyGraph dataset is valuable given scarcity of publicly available data, there was less clarity on description of the dataset, data quality assurance process, and hyperparameters of the selected models. Further, for generalizability of findings, it would be more convincing to present the findings by performing statistical analyses on the distribution of errors rather than showing the average value of the errors. Therefore, this study assessed the supply chain dataset, SupplyGraph, with better clarity on analyses processes, data quality assurance, machine learning (ML) model specifications. After data quality assurance procedures, this study compared performance of Multilayer Perceptions (MLP), Graph Convolution Network (GCN), and Graph Attention Network (GAT) on a demanding forecasting task while matching hyperparameters as feasible as possible. The analyses revealed that GAT performed best, followed by GCN and MLP. Those performance improvements were statistically significant at $\alpha = 0.05$ after correction for multiple comparisons. This study also discussed several considerations in applying GNN to supply chain networks. The current study reinforces the previous study in supply chain benchmark dataset with respect to description of the dataset and methodology, so that the future research in applications of GNN to supply chain becomes more reproducible.