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Found 31 papers, 15 papers with code
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Text-centric visual question answering (VQA) has made great strides with the development of Multimodal Large Language Models (MLLMs), yet open-source models still fall short of leading models like GPT4V and Gemini, partly due to a lack of extensive, high-quality instruction tuning data.
Asphalt Concrete Characterization Using Digital Image Correlation: A Systematic Review of Best Practices, Applications, and Future Vision
This article presents a state-of-art review of DIC as a crucial tool for laboratory testing of asphalt concrete (AC), primarily focusing on the widely utilized 2D-DIC and 3D-DIC techniques.
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach
This study investigates the affecting factors and develops a modified XCMP method to allow automatic thickness prediction of in-service asphalt pavement with non-uniform dielectric properties through depth.
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement Learning
The second objective is to maximize the worst-case spatial robustness value within a bounded time shift.
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity
The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and toxic content.
A Unified Framework for Connecting Noise Modeling to Boost Noise Detection
In this work, we explore the integration of these two approaches, proposing an interconnected structure with three crucial blocks: noise modeling, source knowledge identification, and enhanced noise detection using noise source-knowledge-integration methods.
CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
In this paper, we present a benchmark for investigating channel-adaptive models in microscopy imaging, which consists of 1) a dataset of varied-channel single-cell images, and 2) a biologically relevant evaluation framework.
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor Scenes
In this article, we propose Human-M3, an outdoor multi-modal multi-view multi-person human pose database which includes not only multi-view RGB videos of outdoor scenes but also corresponding pointclouds.
LNL+K: Learning with Noisy Labels and Noise Source Distribution Knowledge
Learning with noisy labels (LNL) is challenging as the model tends to memorize noisy labels, which can lead to overfitting.
USD: Unknown Sensitive Detector Empowered by Decoupled Objectness and Segment Anything Model
Open World Object Detection (OWOD) is a novel and challenging computer vision task that enables object detection with the ability to detect unknown objects.
Membrane Potential Distribution Adjustment and Parametric Surrogate Gradient in Spiking Neural Networks
In SNNs, neural potential distribution tends to deviate unpredictably due to quantization error.
Cross-modal Contrastive Learning for Multimodal Fake News Detection
An attention mechanism with an attention guidance module is implemented to help effectively and interpretably aggregate the aligned unimodal representations and the cross-modality correlations.
Learning the Propagation of Worms in Wireless Sensor Networks
Wireless sensor networks (WSNs) are composed of spatially distributed sensors and are considered vulnerable to attacks by worms and their variants.
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree Problem
The Euclidean Steiner tree problem seeks the min-cost network to connect a collection of target locations, and it underlies many applications of wireless networks.
ReCo: A Dataset for Residential Community Layout Planning
In order to address the issues and advance a benchmark dataset for various intelligent spatial design and analysis applications in the development of smart city, we introduce Residential Community Layout Planning (ReCo) Dataset, which is the first and largest open-source vector dataset related to real-world community to date.
An Effective Transformer-based Solution for RSNA Intracranial Hemorrhage Detection Competition
Nearly all the top solutions rely on 2D convolutional networks and sequential models (Bidirectional GRU or LSTM) to extract intra-slice and inter-slice features, respectively.
Anchoring to Exemplars for Training Mixture-of-Expert Cell Embeddings
Analyzing the morphology of cells in microscopy images can provide insights into the mechanism of compounds or the function of genes.
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT.
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement
With this property, we propose Localization based Reconstruction (LBR) as a strong UVAD baseline and a solid foundation of our solution.
Multi-view Deep One-class Classification: A Systematic Exploration
Third, to remedy the problem that limited benchmark datasets are available for multi-view deep OCC, we extensively collect existing public data and process them into more than 30 new multi-view benchmark datasets via multiple means, so as to provide a publicly available evaluation platform for multi-view deep OCC.
One-Pass Multi-View Clustering for Large-Scale Data
In this way, the generated partition can guide multi-view matrix factorization to produce more purposive coefficient matrix which, as a feedback, improves the quality of partition.
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.
On Training Effective Reinforcement Learning Agents for Real-time Power Grid Operation and Control
Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid.
Optimization and Control Systems and Control Systems and Control
A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification
This paper proposes a self-supervised gait encoding approach that can leverage unlabeled skeleton data to learn gait representations for person Re-ID.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE).
Ranked #14 on
Anomaly Detection
on CUHK Avenue
Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification
Unlike previous methods, we for the first time propose a generic gait encoding approach that can utilize unlabeled skeleton data to learn gait representations in a self-supervised manner.
Evaluating Load Models and Their Impacts on Power Transfer Limits
Since the load dynamics have substantial impacts on power system transient stability, load models are one critical factor that affects the power transfer limits.
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network
Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images.
Neural Network Inference on Mobile SoCs
Mobile devices are empowered with heterogeneous multi-processor Systems-on-Chips (SoCs) to process ML workloads such as Convolutional Neural Network (CNN) inference.
High-Throughput CNN Inference on Embedded ARM big.LITTLE Multi-Core Processors
IoT Edge intelligence requires Convolutional Neural Network (CNN) inference to take place in the edge devices itself.
BEBP: An Poisoning Method Against Machine Learning Based IDSs
In this paper, we adopt the Edge Pattern Detection (EPD) algorithm to design a novel poisoning method that attack against several machine learning algorithms used in IDSs.
