Search Results for author:
Found 29 papers, 10 papers with code
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.
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.
ARtVista: Gateway To Empower Anyone Into Artist
Drawing is an art that enables people to express their imagination and emotions.
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.
Multi-Branch Network for Imagery Emotion Prediction
Experimental results on EMOTIC dataset, which contains large-scale images of people in unconstrained situations labeled with 26 discrete categories of emotions and VAD values, show that our proposed method significantly outperforms state-of-the-art methods with 28. 4% in mAP and 0. 93 in MAE.
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.
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.
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.
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.
Analysis of Master Vein Attacks on Finger Vein Recognition Systems
The results raise the alarm about the robustness of such systems and suggest that master vein attacks should be considered an important security measure.
Rethinking Adversarial Examples for Location Privacy Protection
We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems.
Robust Deepfake On Unrestricted Media: Generation And Detection
Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance.
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions.
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
The dataset is available at https://doi. org/10. 5281/zenodo. 8208877 .
Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio
However, there is still a lack of comprehensive research on both methodologies and datasets.
OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild
To promote these new tasks, we have created the first large-scale dataset posing a high level of challenges that is designed with face-wise rich annotations explicitly for face forgery detection and segmentation, namely OpenForensics.
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.
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.
Fashion-Guided Adversarial Attack on Person Segmentation
It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks.
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
Semantic Instance Meets Salient Object: Study on Video Semantic Salient Instance Segmentation
In addition, to tackle the task of VSSIS, we augment the DAVIS-2017 benchmark dataset by assigning semantic ground-truth for salient instance labels, obtaining SEmantic Salient Instance Video (SESIV) dataset.
Video Salient Object Detection Using Spatiotemporal Deep Features
STCRF is our extension of CRF to the temporal domain and describes the relationships among neighboring regions both in a frame and over frames.
Region-Based Multiscale Spatiotemporal Saliency for Video
Detecting salient objects from a video requires exploiting both spatial and temporal knowledge included in the video.
