All papers

The paper proposes an edge-cloud collaborative inspection framework using multimodal LLMs dynamically scheduled based on scene complexity.

The paper systematically reviews deep learning methods for image anomaly detection in industrial manufacturing inspection.

The authors introduce a training loop where multimodal models use text and visual abilities alongside program execution to generate their own training data.

The paper investigates the gap between raw obfuscated email source and recipient-visible text, evaluating lightweight classifiers under different obfuscations.

This paper introduces an adaptive framework to detect radio jamming attacks within machine learning integrated 5G Open RAN architectures.

This paper uses Bayesian inference combined with machine learning models to detect and classify jamming attacks in 5G networks.

This paper presents a convolutional neural network model to detect and classify facial acne across four areas of the face.

This paper implements a lightweight binary neural network on a RISC-V processor to detect attacks on vehicle CAN bus networks.

This paper presents a dataset, benchmark, and coarse-to-fine learning scheme to detect tiny oriented objects in imagery.

This paper presents a framework and a dataset to generate holistic full-body co-speech gestures from audio and masked movement priors.

The paper models epileptic dynamics using a multi-stable system to detect critical slowing down and design feedback control for seizure prevention.

The paper examines vulnerabilities of deepfake detection models to backdoor attacks and evaluates related defense strategies.

The paper studies cybersecurity threat detection using sequential deep learning models evaluated on the KDD network traffic dataset.

The paper summary contains webpage metadata rather than research text, though it mentions partial discharge localization for drone inspection using UHF detection.

The paper evaluates machine learning models, finding Random Forest predicts depression in menopausal women with high accuracy.

The paper proposes parallel delay chains to enable low-power neuromorphic circuits to detect anomalies in long electrocardiogram signals.

The paper provides a synthetic dataset to detect passive wireless jamming attacks carried out using reconfigurable intelligent surfaces.

The authors introduce CharXiv, a benchmark of realistic scientific charts designed to test descriptive and reasoning capabilities in multimodal language models.

This paper proposes a discrete diffusion model for controllable visual layout generation under structural element constraints.

This paper introduces an evaluation benchmark using multimodal models to assess generated videos across human preference dimensions.

This paper analyzes visual token utilization in high-resolution vision-language models to optimize token pruning across network layers.

This paper evaluates an ensemble deep learning framework of two convolutional networks for robust and efficient deepfake video detection.

The paper proposes a modular vision-and-language navigation framework combining dynamic memory pruning and language-model spatial reasoning.

The paper introduces a decomposed attention mechanism for vision-language models to separate visual and textual token processing.

The paper presents an open-source multimodal language model combining a small language backbone with vision encoders for efficient inference.

Introduces a mixture-of-prompts distillation method to transfer knowledge from hard prompts to soft prompts, improving generalization on unseen classes.

Proposes a distillation framework and structural loss to speed up diffusion-based 3D LiDAR scene completion for autonomous driving perception.

The paper develops a time-frequency analysis method to diagnose partial discharge acoustic signals in industrial inspection equipment.

The paper investigates token pruning strategies to accelerate Vision Transformer inference for object detection and instance segmentation.

The paper presents a cross-attention deep learning framework fusing real-time vital signs and motion sensor data for medical anomaly detection.

The paper introduces an inference-time framework that creates inspection plans to guide LLMs in detecting source code vulnerabilities.

The paper proposes a frequency-enhanced conditional VAE combined with graph neural networks for anomaly detection in industrial multivariate time series.

The paper presents an autonomous patrol robot combining mapping and computer vision to detect threats and manage security incidents.