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Found 27 papers, 17 papers with code
Little Strokes Fell Great Oaks: Boosting the Hierarchical Features for Multi-exposure Image Fusion
In recent years, deep learning networks have made remarkable strides in the domain of multi-exposure image fusion.
Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging
Model merging is a promising lightweight model empowerment technique that does not rely on expensive computing devices (e. g., GPUs) or require the collection of specific training data.
Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks
Given a pair of captured images, subtle perturbations and distortions which go unnoticed by the human visual system tend to attack the correspondence matching, impairing the performance of image stitching algorithms.
From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion
With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks.
Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning
Although image captioning models have made significant advancements in recent years, the majority of them heavily depend on high-quality datasets containing paired images and texts which are costly to acquire.
FigStep: Jailbreaking Large Vision-language Models via Typographic Visual Prompts
Ensuring the safety of artificial intelligence-generated content (AIGC) is a longstanding topic in the artificial intelligence (AI) community, and the safety concerns associated with Large Language Models (LLMs) have been widely investigated.
Trash to Treasure: Low-Light Object Detection via Decomposition-and-Aggregation
In this work, we try to arouse the potential of enhancer + detector.
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks.
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
To overcome these limitations, we propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
Improving Misaligned Multi-modality Image Fusion with One-stage Progressive Dense Registration
Both stages directly estimate the respective target deformation fields.
PAIF: Perception-Aware Infrared-Visible Image Fusion for Attack-Tolerant Semantic Segmentation
We first conduct systematic analyses about the components of image fusion, investigating the correlation with segmentation robustness under adversarial perturbations.
Ranked #16 on
Thermal Image Segmentation
on MFN Dataset
Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion
Finally, we merge all graph features to get the fusion result.
Bilevel Generative Learning for Low-Light Vision
In this study, we propose a generic low-light vision solution by introducing a generative block to convert data from the RAW to the RGB domain.
Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation
Multi-modality image fusion and segmentation play a vital role in autonomous driving and robotic operation.
Ranked #3 on
Semantic Segmentation
on FMB Dataset
WaterFlow: Heuristic Normalizing Flow for Underwater Image Enhancement and Beyond
Underwater images suffer from light refraction and absorption, which impairs visibility and interferes the subsequent applications.
Multi-Spectral Image Stitching via Spatial Graph Reasoning
Multi-spectral image stitching leverages the complementarity between infrared and visible images to generate a robust and reliable wide field-of-view (FOV) scene.
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes (i. e., freezing the encoder in the adaptation and testing phases).
A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion
Qualitative and quantitative experimental results on different categories of image fusion problems and related downstream tasks (e. g., visual enhancement and semantic understanding) substantiate the flexibility and effectiveness of our TIM.
Embracing Compact and Robust Architectures for Multi-Exposure Image Fusion
In recent years, deep learning-based methods have achieved remarkable progress in multi-exposure image fusion.
MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes
3D object detection plays a crucial role in numerous intelligent vision systems.
An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection
Their common characteristic of seeking complementary cues from different source images motivates us to explore the collaborative relationship between Fusion and Salient object detection tasks on infrared and visible images via an Interactively Reinforced multi-task paradigm for the first time, termed IRFS.
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Recently, multi-modality scene perception tasks, e. g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems.
Breaking Modality Disparity: Harmonized Representation for Infrared and Visible Image Registration
Since the differences in viewing range, resolution and relative position, the multi-modality sensing module composed of infrared and visible cameras needs to be registered so as to have more accurate scene perception.
Breaking Free from Fusion Rule: A Fully Semantic-driven Infrared and Visible Image Fusion
To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules.
CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion
Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors.
Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration
Moreover, to better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM) to adaptively select more meaningful features for fusion in the Dual-path Interaction Fusion Network (DIFN).
Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection
This study addresses the issue of fusing infrared and visible images that appear differently for object detection.
Ranked #1 on
Object Detection
on Multispectral Dataset
