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Found 25 papers, 10 papers with code
Hypergraph Foundation Model
Hypergraph neural networks (HGNNs) effectively model complex high-order relationships in domains like protein interactions and social networks by connecting multiple vertices through hyperedges, enhancing modeling capabilities, and reducing information loss.
Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging
This study gives impetus to enhance both NIR images and events, which paves the way for high-fidelity low-light imaging and neuromorphic reasoning.
Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting in Anomaly Detection
Unsupervised Continuous Anomaly Detection (UCAD) faces significant challenges in multi-task representation learning, with existing methods suffering from incomplete representation and catastrophic forgetting.
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Unlike existing control methods that require a user to specify attributes for each edit direction individually, SliderSpace discovers multiple interpretable and diverse directions simultaneously from a single text prompt.
HyperDefect-YOLO: Enhance YOLO with HyperGraph Computation for Industrial Defect Detection
HD-YOLO consists of Defect Aware Module (DAM) and Mixed Graph Network (MGNet) in the backbone, which specialize for perception and extraction of defect features.
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIP
Then these modality-specific embeddings are used to enhance original representations of CLIP for better matching ability.
TSUBF-Net: Trans-Spatial UNet-like Network with Bi-direction Fusion for Segmentation of Adenoid Hypertrophy in CT
The results in the other two public datasets also demonstrate that our methods can robustly and effectively address the challenges of 3D segmentation in CT scans.
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection
We focus on 3D UIAD and multimodal UIAD, providing a comprehensive summary of unsupervised industrial anomaly detection in three modal settings.
Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?
Existing benchmarks like NLGraph and GraphQA evaluate LLMs on graphs by focusing mainly on pairwise relationships, overlooking the high-order correlations found in real-world data.
TurboEdit: Instant text-based image editing
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models.
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation
Specifically, we synthesize anomalous images on given normal images as sample pairs to adapt CLIP for 3D anomaly classification and segmentation.
Anomaly Multi-classification in Industrial Scenarios: Transferring Few-shot Learning to a New Task
In industrial scenarios, it is crucial not only to identify anomalous items but also to classify the type of anomaly.
Lazy Diffusion Transformer for Interactive Image Editing
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
Saliency-Aware Regularized Graph Neural Network
In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone.
Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection
Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning.
Edge-aware Plug-and-play Scheme for Semantic Segmentation
Therefore, we propose an abstract and universal edge supervision method called Edge-aware Plug-and-play Scheme (EPS), which can be easily and quickly applied to any semantic segmentation models.
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis
In the forward stage, we take advantage of LTH with rewinding weights to progressively shrink the SR model and the pruning-out masks that form nested sets.
Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks
As a fundamental computer vision task, crowd counting plays an important role in public safety.
Harmonizing output imbalance for defect segmentation on extremely-imbalanced photovoltaic module cells images
This extreme imbalance makes it difficult to segment the THC of PV module cells, which is also a challenge for semantic segmentation.
Variational Distillation for Multi-View Learning
Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions.
Third Time's the Charm? Image and Video Editing with StyleGAN3
In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.
StyleAlign: Analysis and Applications of Aligned StyleGAN Models
Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation.
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works.
Maximum Correntropy Unscented Filter
To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF).
