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Found 14 papers, 10 papers with code
Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation
Referring Remote Sensing Image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing, delineating specific regions in aerial images as described by textual queries.
X-Dreamer: Creating High-quality 3D Content by Bridging the Domain Gap Between Text-to-2D and Text-to-3D Generation
Nevertheless, a substantial domain gap exists between 2D images and 3D assets, primarily attributed to variations in camera-related attributes and the exclusive presence of foreground objects.
Robust Waveform Design for Integrated Sensing and Communication
Therefore, we formulate robust waveform design problems by studying the worst-case channels and prove that the robustly-estimated performance is guaranteed to be attainable in real-world operation.
Semi-Supervised Panoptic Narrative Grounding
Remarkably, our SS-PNG-NW+ outperforms fully-supervised models with only 30% and 50% supervision data, exceeding their performance by 0. 8% and 1. 1% respectively.
NICE: Improving Panoptic Narrative Detection and Segmentation with Cascading Collaborative Learning
To address this, we introduce two cascading modules based on the barycenter of the mask, which are Coordinate Guided Aggregation (CGA) and Barycenter Driven Localization (BDL), responsible for segmentation and detection, respectively.
JM3D & JM3D-LLM: Elevating 3D Understanding with Joint Multi-modal Cues
The rising importance of 3D understanding, pivotal in computer vision, autonomous driving, and robotics, is evident.
3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation
In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions.
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
Insufficient synergy neglects the idea that a robust 3D representation should align with the joint vision-language space, rather than independently aligning with each modality.
X-Mesh: Towards Fast and Accurate Text-driven 3D Stylization via Dynamic Textual Guidance
Text-driven 3D stylization is a complex and crucial task in the fields of computer vision (CV) and computer graphics (CG), aimed at transforming a bare mesh to fit a target text.
Learning Against Distributional Uncertainty: On the Trade-off Between Robustness and Specificity
Trustworthy machine learning aims at combating distributional uncertainties in training data distributions compared to population distributions.
Towards Real-Time Panoptic Narrative Grounding by an End-to-End Grounding Network
Extensive experiments on the PNG benchmark dataset demonstrate the effectiveness and efficiency of our method.
Distributional Robustness Bounds Generalization Errors
Third, we show that generalization errors of machine learning models can be characterized using the distributional uncertainty of the nominal distribution and the robustness measures of these machine learning models, which is a new perspective to bound generalization errors, and therefore, explain the reason why distributionally robust machine learning models, Bayesian models, and regularization models tend to have smaller generalization errors in a unified manner.
A model aggregation approach for high-dimensional large-scale optimization
In this study, we propose a model aggregation method in the Bayesian optimization (MamBO) algorithm for efficiently solving high-dimensional large-scale optimization problems.
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization
To adapt the EI for better performance under cumulative regret, we introduce a novel quantity called the evaluation cost which is compared against the acquisition function, and with this, develop the expected improvement-cost (EIC) algorithm.
