Search Results for author:
Found 15 papers, 9 papers with code
PosSAM: Panoptic Open-vocabulary Segment Anything
In this paper, we introduce an open-vocabulary panoptic segmentation model that effectively unifies the strengths of the Segment Anything Model (SAM) with the vision-language CLIP model in an end-to-end framework.
Ranked #1 on
Open Vocabulary Panoptic Segmentation
on ADE20K
Open Vocabulary Panoptic Segmentation
Open Vocabulary Semantic Segmentation
+2
DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction
Next, we use a conditional regenerator, which takes the redacted image and the dense predictions as inputs, and reconstructs the original image by filling in the missing structural information.
TransAdapt: A Transformative Framework for Online Test Time Adaptive Semantic Segmentation
Test-time adaptive (TTA) semantic segmentation adapts a source pre-trained image semantic segmentation model to unlabeled batches of target domain test images, different from real-world, where samples arrive one-by-one in an online fashion.
Test-time Adaptation vs. Training-time Generalization: A Case Study in Human Instance Segmentation using Keypoints Estimation
In this approach, we do not assume test-time access to the labeled source dataset.
Panoptic, Instance and Semantic Relations: A Relational Context Encoder to Enhance Panoptic Segmentation
A Panoptic Relational Attention (PRA) module is then applied to the encodings and the global feature map from the backbone.
Real-Time, Accurate, and Consistent Video Semantic Segmentation via Unsupervised Adaptation and Cross-Unit Deployment on Mobile Device
Such a deployment scheme best utilizes the available processing power on the smartphone and enables real-time operation of our adaptive video segmentation algorithm.
Learning Dynamic Network Using a Reuse Gate Function in Semi-supervised Video Object Segmentation
Current state-of-the-art approaches for Semi-supervised Video Object Segmentation (Semi-VOS) propagates information from previous frames to generate segmentation mask for the current frame.
TTVOS: Lightweight Video Object Segmentation with Adaptive Template Attention Module and Temporal Consistency Loss
Our template matching method consists of short-term and long-term matching.
SINet: Extreme Lightweight Portrait Segmentation Networks with Spatial Squeeze Modules and Information Blocking Decoder
To solve the first problem, we introduce the new extremely lightweight portrait segmentation model SINet, containing an information blocking decoder and spatial squeeze modules.
Ranked #1 on
Portrait Segmentation
on EG1800
ExtremeC3Net: Extreme Lightweight Portrait Segmentation Networks using Advanced C3-modules
In our qualitative and quantitative analysis on the EG1800 dataset, we show that our method outperforms various existing lightweight segmentation models.
A Comprehensive Overhaul of Feature Distillation
We investigate the design aspects of feature distillation methods achieving network compression and propose a novel feature distillation method in which the distillation loss is designed to make a synergy among various aspects: teacher transform, student transform, distillation feature position and distance function.
Ranked #38 on
Knowledge Distillation
on ImageNet
C3: Concentrated-Comprehensive Convolution and its application to semantic segmentation
To resolve this problem, we propose a new block called Concentrated-Comprehensive Convolution (C3) which applies the asymmetric convolutions before the depth-wise separable dilated convolution to compensate for the information loss due to dilated convolution.
MC-GAN: Multi-conditional Generative Adversarial Network for Image Synthesis
This block enables MC-GAN to generate a realistic object image with the desired background by controlling the amount of the background information from the given base image using the foreground information from the text attributes.
Superpixel-based Semantic Segmentation Trained by Statistical Process Control
Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks.
Enhancement of SSD by concatenating feature maps for object detection
In this paper, we propose and analyze how to use feature maps effectively to improve the performance of the conventional SSD.
