Search Results for author:
Found 7 papers, 4 papers with code
VRP-SAM: SAM with Visual Reference Prompt
In this paper, we propose a novel Visual Reference Prompt (VRP) encoder that empowers the Segment Anything Model (SAM) to utilize annotated reference images as prompts for segmentation, creating the VRP-SAM model.
Exploring Effective Factors for Improving Visual In-Context Learning
By doing this, the model can leverage the diverse knowledge stored in different parts of the model to improve its performance on new tasks.
s-Adaptive Decoupled Prototype for Few-Shot Object Detection
To provide precise information for the query image, the prototype is decoupled into task-specific ones, which provide tailored guidance for 'where to look' and 'what to look for', respectively.
Self-Supervised Guided Segmentation Framework for Unsupervised Anomaly Detection
In this paper, a novel Self-Supervised Guided Segmentation Framework (SGSF) is proposed by jointly exploring effective generation method of forged anomalous samples and the normal sample features as the guidance information of segmentation for anomaly detection.
Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning
In this paper, we rethink the paradigm and explore a new regime: {\em fine-tuning a small part of parameters in the backbone}.
Ranked #8 on
Few-Shot Semantic Segmentation
on COCO-20i (1-shot)
SSA: Semantic Structure Aware Inference for Weakly Pixel-Wise Dense Predictions without Cost
The pixel-wise dense prediction tasks based on weakly supervisions currently use Class Attention Maps (CAM) to generate pseudo masks as ground-truth.
Weakly-Supervised Object Localization
Weakly supervised Semantic Segmentation
+1
CTNet: Context-based Tandem Network for Semantic Segmentation
Specifically, the Spatial Contextual Module (SCM) is leveraged to uncover the spatial contextual dependency between pixels by exploring the correlation between pixels and categories.
Ranked #71 on
Semantic Segmentation
on ADE20K val
