Search Results for author:
Found 50 papers, 23 papers with code
Improving Referring Image Segmentation using Vision-Aware Text Features
Our method involves using CLIP to derive a CLIP Prior that integrates an object-centric visual heatmap with text description, which can be used as the initial query in DETR-based architecture for the segmentation task.
Cluster-based Video Summarization with Temporal Context Awareness
In this paper, we present TAC-SUM, a novel and efficient training-free approach for video summarization that addresses the limitations of existing cluster-based models by incorporating temporal context.
Ranked #1 on
Unsupervised Video Summarization
on SumMe
Enhancing Video Summarization with Context Awareness
Despite the importance of video summarization, there is a lack of diverse and representative datasets, hindering comprehensive evaluation and benchmarking of algorithms.
Ranked #8 on
Unsupervised Video Summarization
on SumMe
Ensemble Learning for Vietnamese Scene Text Spotting in Urban Environments
This paper presents a simple yet efficient ensemble learning framework for Vietnamese scene text spotting.
ARtVista: Gateway To Empower Anyone Into Artist
Drawing is an art that enables people to express their imagination and emotions.
iCONTRA: Toward Thematic Collection Design Via Interactive Concept Transfer
With a user-friendly interface, iCONTRA enables both experienced designers and novices to effortlessly explore creative design concepts and efficiently generate thematic collections.
TSRNet: Simple Framework for Real-time ECG Anomaly Detection with Multimodal Time and Spectrogram Restoration Network
As a result, we introduce a specialized network called the Multimodal Time and Spectrogram Restoration Network (TSRNet) designed specifically for detecting anomalies in ECG signals.
NearbyPatchCL: Leveraging Nearby Patches for Self-Supervised Patch-Level Multi-Class Classification in Whole-Slide Images
In addressing the demands of this critical task, self-supervised learning (SSL) methods have emerged as a valuable resource, leveraging their efficiency in circumventing the need for a large number of annotations, which can be both costly and time-consuming to deploy supervised methods.
PGS: Pose-Guided Supervision for Mitigating Clothes-Changing in Person Re-Identification
Person Re-Identification (Re-ID) task seeks to enhance the tracking of multiple individuals by surveillance cameras.
Clothes Changing Person Re-Identification
Person Retrieval
+2
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
SAM3D: Segment Anything Model in Volumetric Medical Images
Image segmentation remains a pivotal component in medical image analysis, aiding in the extraction of critical information for precise diagnostic practices.
MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation
MEGANet is designed as an end-to-end framework, encompassing three key modules: an encoder, which is responsible for capturing and abstracting the features from the input image, a decoder, which focuses on salient features, and the Edge-Guided Attention module (EGA) that employs the Laplacian Operator to accentuate polyp boundaries.
Unveiling Camouflage: A Learnable Fourier-based Augmentation for Camouflaged Object Detection and Instance Segmentation
Camouflaged object detection (COD) and camouflaged instance segmentation (CIS) aim to recognize and segment objects that are blended into their surroundings, respectively.
Few-Shot Object Detection via Synthetic Features with Optimal Transport
Our overarching goal is to train a generator that captures the data variations of the base dataset.
DM-VTON: Distilled Mobile Real-time Virtual Try-On
Additionally, we propose Virtual Try-on-guided Pose for Data Synthesis to address the limited pose variation observed in training images.
VIDES: Virtual Interior Design via Natural Language and Visual Guidance
The system successfully captures the essence of users' descriptions while providing flexibility for customization.
M^2UNet: MetaFormer Multi-scale Upsampling Network for Polyp Segmentation
Polyp segmentation has recently garnered significant attention, and multiple methods have been formulated to achieve commendable outcomes.
The Art of Camouflage: Few-shot Learning for Animal Detection and Segmentation
In this paper, we address the problem of few-shot learning for camouflaged object detection and segmentation.
SketchANIMAR: Sketch-based 3D Animal Fine-Grained Retrieval
To this end, we introduce a novel SHREC challenge track that focuses on retrieving relevant 3D animal models from a dataset using sketch queries and expedites accessing 3D models through available sketches.
TextANIMAR: Text-based 3D Animal Fine-Grained Retrieval
Unlike previous SHREC challenge tracks, the proposed task is considerably more challenging, requiring participants to develop innovative approaches to tackle the problem of text-based retrieval.
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
To overcome the disadvantage of the point estimation mechanism, we propose a novel approach, dubbed MaskDiff, which models the underlying conditional distribution of a binary mask, which is conditioned on an object region and $K-$shot information.
Multi Kernel Positional Embedding ConvNeXt for Polyp Segmentation
Specifically, with the increase in cases, the diagnosis and identification need to be faster and more accurate for many patients; in endoscopic images, the segmentation task has been vital to helping the doctor identify the position of the polyps or the ache in the system correctly.
Multilingual Communication System with Deaf Individuals Utilizing Natural and Visual Languages
To bridge this language barrier, we propose a novel multilingual communication system, namely MUGCAT, to improve the communication efficiency of sign language users.
EmbryosFormer: Deformable Transformer and Collaborative Encoding-Decoding for Embryos Stage Development Classification
The transformer expanding path models the temporal coherency between embryo images to ensure monotonic non-decreasing constraint and is optimized by a segmentation head.
AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation
PMR module represents each video snippet by a visual-linguistic feature, in which main actors and surrounding environment are represented by visual information, whereas relevant objects are depicted by linguistic features through an image-text model.
SHREC 2022 Track on Online Detection of Heterogeneous Gestures
This paper presents the outcomes of a contest organized to evaluate methods for the online recognition of heterogeneous gestures from sequences of 3D hand poses.
SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wild
We define two SBSR tasks and construct two benchmarks consisting of more than 46, 000 CAD models, 1, 700 realistic models, and 145, 000 sketches in total.
SHREC 2022: pothole and crack detection in the road pavement using images and RGB-D data
A training set, composed of 3836 semantic segmentation image/mask pairs and 797 RGB-D video clips collected with the latest depth cameras was made available to the participants.
Ranked #1 on
Semantic Segmentation
on Pothole Mix
An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices
Using our proposed model architecture, we compare the accuracy between stress detection models that use measures from each individual signal source, and one model employing the fusion of multiple sensor sources.
Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices
The results from the experiment show that training the model with (comparatively low-cost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal.
ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation
Temporal action proposal generation (TAPG) aims to estimate temporal intervals of actions in untrimmed videos, which is a challenging yet plays an important role in many tasks of video analysis and understanding.
Meta-Learning of NAS for Few-shot Learning in Medical Image Applications
To address such limitations, meta-learning has been adopted in the scenarios of few-shot learning and multiple tasks.
DAM-AL: Dilated Attention Mechanism with Attention Loss for 3D Infant Brain Image Segmentation
While Magnetic Resonance Imaging (MRI) has played an essential role in infant brain analysis, segmenting MRI into a number of tissues such as gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) is crucial and complex due to the extremely low intensity contrast between tissues at around 6-9 months of age as well as amplified noise, myelination, and incomplete volume.
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
The dataset is available at https://doi. org/10. 5281/zenodo. 8208877 .
AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation
In this paper, we make an attempt to simulate that ability of a human by proposing Actor Environment Interaction (AEI) network to improve the video representation for temporal action proposals generation.
Agent-Environment Network for Temporal Action Proposal Generation
Temporal action proposal generation is an essential and challenging task that aims at localizing temporal intervals containing human actions in untrimmed videos.
SHREC 2021: Track on Skeleton-based Hand Gesture Recognition in the Wild
Gesture recognition is a fundamental tool to enable novel interaction paradigms in a variety of application scenarios like Mixed Reality environments, touchless public kiosks, entertainment systems, and more.
Dictionary-Guided Scene Text Recognition
Language prior plays an important role in the way humans perceive and recognize text in the wild.
DyGLIP: A Dynamic Graph Model with Link Prediction for Accurate Multi-Camera Multiple Object Tracking
Multi-Camera Multiple Object Tracking (MC-MOT) is a significant computer vision problem due to its emerging applicability in several real-world applications.
Contextual Guided Segmentation Framework for Semi-supervised Video Instance Segmentation
For human instance, we develop skeleton-guided segmentation in a frame along with object flow to correct and refine the result across frames.
Refined Deep Neural Network and U-Net for Polyps Segmentation
The Medico: Multimedia Task 2020 focuses on developing an efficient and accurate computer-aided diagnosis system for automatic segmentation [3].
Anabranch Network for Camouflaged Object Segmentation
Different from existing networks for segmentation, our proposed network possesses the second branch for classification to predict the probability of containing camouflaged object(s) in an image, which is then fused into the main branch for segmentation to boost up the segmentation accuracy.
SHREC 2021: Retrieval and classification of protein surfaces equipped with physical and chemical properties
This paper presents the methods that have participated in the SHREC 2021 contest on retrieval and classification of protein surfaces on the basis of their geometry and physicochemical properties.
Camouflaged Instance Segmentation In-The-Wild: Dataset, Method, and Benchmark Suite
We also provide a benchmark suite for the task of camouflaged instance segmentation.
MirrorNet: Bio-Inspired Camouflaged Object Segmentation
Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively.
Ranked #8 on
Camouflaged Object Segmentation
on CAMO
Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement
Chest X-ray examination plays an important role in lung disease detection.
Image Alignment in Unseen Domains via Domain Deep Generalization
This paper presents a novel deep learning based approach to tackle the problem of across unseen modalities.
Domain Generalization via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
Fast Flow Reconstruction via Robust Invertible nxn Convolution
The experiments on CIFAR-10, ImageNet and Celeb-HQ datasets, have shown that our invertible $n \times n$ convolution helps to improve the performance of generative models significantly.
Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models
However, it has been largely overlooked in the problem of recognition in new unseen domains.
