Search Results for author:
Found 12 papers, 10 papers with code
ZIM: Zero-Shot Image Matting for Anything
To address this limitation, we propose a novel zero-shot image matting model, called ZIM, with two key contributions: First, we develop a label converter that transforms segmentation labels into detailed matte labels, constructing the new SA1B-Matte dataset without costly manual annotations.
HyperCLOVA X Technical Report
We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding.
Rethinking Saliency-Guided Weakly-Supervised Semantic Segmentation
This paper presents a fresh perspective on the role of saliency maps in weakly-supervised semantic segmentation (WSSS) and offers new insights and research directions based on our empirical findings.
Towards Label-Efficient Human Matting: A Simple Baseline for Weakly Semi-Supervised Trimap-Free Human Matting
To address this challenge, we introduce a new learning paradigm, weakly semi-supervised human matting (WSSHM), which leverages a small amount of expensive matte labels and a large amount of budget-friendly segmentation labels, to save the annotation cost and resolve the domain generalization problem.
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning
Panoptic segmentation, combining semantic and instance segmentation, stands as a cutting-edge computer vision task.
The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation
In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation.
EResFD: Rediscovery of the Effectiveness of Standard Convolution for Lightweight Face Detection
Based on our observation, we propose to employ ResNet with a highly reduced channel, which surprisingly allows high efficiency compared to other mobile-friendly networks (e. g., MobileNetV1, V2, V3).
Learning Features with Parameter-Free Layers
We aim to break the stereotype of organizing the spatial operations of building blocks into trainable layers.
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement
This semantic drift occurs confusion between background and instance in training and consequently degrades the segmentation performance.
Image-level Supervised Instance Segmentation
Point-Supervised Instance Segmentation
+5
SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning
While the recent CISS algorithms utilize variants of the knowledge distillation (KD) technique to tackle the problem, they failed to fully address the critical challenges in CISS causing the catastrophic forgetting; the semantic drift of the background class and the multi-label prediction issue.
Ranked #1 on
Disjoint 10-1
on PASCAL VOC 2012
class-incremental learning
Continual Semantic Segmentation
+13
TricubeNet: 2D Kernel-Based Object Representation for Weakly-Occluded Oriented Object Detection
We present a novel approach for oriented object detection, named TricubeNet, which localizes oriented objects using visual cues ($i. e.,$ heatmap) instead of oriented box offsets regression.
Discriminative Region Suppression for Weakly-Supervised Semantic Segmentation
However, since localization maps obtained from the classifier focus only on sparse discriminative object regions, it is difficult to generate high-quality segmentation labels.
