Search Results for author:
Found 16 papers, 9 papers with code
K-HATERS: A Hate Speech Detection Corpus in Korean with Target-Specific Ratings
This resource is the largest offensive language corpus in Korean and is the first to offer target-specific ratings on a three-point Likert scale, enabling the detection of hate expressions in Korean across varying degrees of offensiveness.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets.
Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning
Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information.
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method.
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images.
Ranked #29 on
Anomaly Detection
on MVTec AD
Unsupervised Video Object Segmentation via Prototype Memory Network
The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps.
Ranked #5 on
Unsupervised Video Object Segmentation
on FBMS test
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
Pixel-Level Equalized Matching for Video Object Segmentation
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation.
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps.
Ranked #3 on
RGB-D Salient Object Detection
on NJU2K
Tackling Background Distraction in Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos.
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection
Moreover, MOLoss urges the model to focus on learning normal objects captured within RandomSEMO by amplifying the loss on the pixels near the moving objects.
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss
1) Indiscriminately integrating the encoder feature, which contains spatial information for multiple objects, and the decoder feature, which contains global information of the salient object, is likely to convey unnecessary details of non-salient objects to the decoder, hindering saliency detection.
Ranked #1 on
RGB Salient Object Detection
on PASCAL-S
EdgeConv with Attention Module for Monocular Depth Estimation
Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential.
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos.
Anomaly Detection In Surveillance Videos
Optical Flow Estimation
+1
LFI-CAM: Learning Feature Importance for Better Visual Explanation
LFI-CAM generates an attention map for visual explanation during forward propagation, at the same time, leverages the attention map to improve the classification performance through the attention mechanism.
