Search Results for author:
Found 17 papers, 5 papers with code
Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e. g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models.
COCONut: Modernizing COCO Segmentation
By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5. 18M panoptic masks, we introduce COCONut, the COCO Next Universal segmenTation dataset.
MaXTron: Mask Transformer with Trajectory Attention for Video Panoptic Segmentation
To alleviate the issue, we propose to adapt the trajectory attention for both the dense pixel features and object queries, aiming to improve the short-term and long-term tracking results, respectively.
Ranked #1 on
Video Panoptic Segmentation
on VIPSeg
Selective Feature Adapter for Dense Vision Transformers
Specifically, SFA consists of external adapters and internal adapters which are sequentially operated over a transformer model.
Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP
The proposed FC-CLIP, benefits from the following observations: the frozen CLIP backbone maintains the ability of open-vocabulary classification and can also serve as a strong mask generator, and the convolutional CLIP generalizes well to a larger input resolution than the one used during contrastive image-text pretraining.
Ranked #1 on
Open Vocabulary Semantic Segmentation
on Cityscapes
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+1
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval
The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts.
GeoAI at ACM SIGSPATIAL: The New Frontier of Geospatial Artificial Intelligence Research
Geospatial Artificial Intelligence (GeoAI) is an interdisciplinary field enjoying tremendous adoption.
NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night
Notably, NightLab contains models at two levels of granularity, i. e. image and regional, and each level is composed of light adaptation and segmentation modules.
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization
Specifically, given a pair of student and teacher networks, DistPro first sets up a rich set of KD connection from the transmitting layers of the teacher to the receiving layers of the student, and in the meanwhile, various transforms are also proposed for comparing feature maps along its pathway for the distillation.
AutoAdapt: Automated Segmentation Network Search for Unsupervised Domain Adaptation
Neural network-based semantic segmentation has achieved remarkable results when large amounts of annotated data are available, that is, in the supervised case.
Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery
Land-cover classification using remote sensing imagery is an important Earth observation task.
Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling
Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on.
Using Conditional Generative Adversarial Networks to Generate Ground-Level Views From Overhead Imagery
This paper develops a deep-learning framework to synthesize a ground-level view of a location given an overhead image.
Region Convolutional Features for Multi-Label Remote Sensing Image Retrieval
In our approach, we first train a FCN model using a pixel-wise labeled dataset, and the trained FCN is then used to predict the segmentation maps of each image in the considered archive.
What Is It Like Down There? Generating Dense Ground-Level Views and Image Features From Overhead Imagery Using Conditional Generative Adversarial Networks
More significantly, we show the generated images are representative of the locations and that the representations learned by the cGANs are informative.
Spatial Morphing Kernel Regression For Feature Interpolation
We also show that the spatial morphing kernel improves the results.
Fine-Grained Land Use Classification at the City Scale Using Ground-Level Images
We perform fine-grained land use mapping at the city scale using ground-level images.
