Search Results for author:
Found 33 papers, 16 papers with code
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection
Zero-shot anomaly detection (ZSAD) aims to identify anomalies in unseen categories by leveraging CLIP's zero-shot capabilities to match text prompts with visual features.
CoMoGaussian: Continuous Motion-Aware Gaussian Splatting from Motion-Blurred Images
3D Gaussian Splatting (3DGS) has gained significant attention for their high-quality novel view rendering, motivating research to address real-world challenges.
Find First, Track Next: Decoupling Identification and Propagation in Referring Video Object Segmentation
This reference is then utilized by a dedicated propagation module to track and segment the object across the entire video.
Ranked #1 on
Referring Video Object Segmentation
on Ref-DAVIS17
CoCoGaussian: Leveraging Circle of Confusion for Gaussian Splatting from Defocused Images
While conventional methods depend on sharp images for accurate scene reconstruction, real-world scenarios are often affected by defocus blur due to finite depth of field, making it essential to account for realistic 3D scene representation.
Elevating Flow-Guided Video Inpainting with Reference Generation
Powered by a strong generative model, our method not only significantly enhances frame-level quality for object removal but also synthesizes new content in the missing areas based on user-provided text prompts.
Ranked #1 on
Video Inpainting
on HQVI (2K)
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation
For static area, the Masked Static (MS) module enhances temporal consistency by focusing on stable regions.
Effective SAM Combination for Open-Vocabulary Semantic Segmentation
Open-vocabulary semantic segmentation aims to assign pixel-level labels to images across an unlimited range of classes.
Transforming Static Images Using Generative Models for Video Salient Object Detection
This ability allows the model to generate plausible optical flows, preserving semantic integrity while reflecting the independent motion of scene elements.
Ranked #1 on
Video Salient Object Detection
on DAVSOD-easy35
(using extra training data)
Video Diffusion Models are Strong Video Inpainter
However, it has limitations such as the inaccuracy of optical flow prediction and the propagation of noise over time.
Improving Unsupervised Video Object Segmentation via Fake Flow Generation
Unsupervised video object segmentation (VOS), also known as video salient object detection, aims to detect the most prominent object in a video at the pixel level.
Ranked #1 on
Unsupervised Video Object Segmentation
on FBMS test
CRiM-GS: Continuous Rigid Motion-Aware Gaussian Splatting from Motion-Blurred Images
3D Gaussian Splatting (3DGS) has gained significant attention for their high-quality novel view rendering, motivating research to address real-world challenges.
Synchronizing Vision and Language: Bidirectional Token-Masking AutoEncoder for Referring Image Segmentation
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
Ranked #3 on
Unsupervised Video Object Segmentation
on FBMS test
Adaptive Graph Convolution Module for Salient Object Detection
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image.
Guided Slot Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
One-Shot Video Inpainting
Usually, a video sequence and object segmentation masks for all frames are required as the input for this task.
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.
Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning
Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowded scenes.
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
In this paper, we propose the Spatio-Temporal Curve Network (STC-Net) to effectively leverage the spatio-temporal dependency of the human skeleton.
Dual Prototype Attention for Unsupervised Video Object Segmentation
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
Ranked #2 on
Unsupervised Video Object Segmentation
on FBMS test
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 #46 on
Anomaly Detection
on MVTec AD
(using extra training data)
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 #9 on
Unsupervised Video Object Segmentation
on FBMS test
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.
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.
Ranked #6 on
Unsupervised Video Object Segmentation
on FBMS test
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 #4 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.
Pixel-Level Bijective Matching for Video Object Segmentation
Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced.
Multi-object tracking with self-supervised associating network
So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem.
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features
For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features through normalizing flow (NF)-based generative models to learn the tractable likelihoods and identify anomalies using out of distribution detection.
PMVOS: Pixel-Level Matching-Based Video Object Segmentation
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided.
CRVOS: Clue Refining Network for Video Object Segmentation
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances.
Ranked #60 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
