Search Results for author:
Found 29 papers, 7 papers with code
Macro2Micro: Cross-modal Magnetic Resonance Imaging Synthesis Leveraging Multi-scale Brain Structures
Spanning multiple scales-from macroscopic anatomy down to intricate microscopic architecture-the human brain exemplifies a complex system that demands integrated approaches to fully understand its complexity.
A Training-Free Approach for Music Style Transfer with Latent Diffusion Models
Music style transfer, while offering exciting possibilities for personalized music generation, often requires extensive training or detailed textual descriptions.
Revisiting Your Memory: Reconstruction of Affect-Contextualized Memory via EEG-guided Audiovisual Generation
In this paper, we introduce RecallAffectiveMemory, a novel task designed to reconstruct autobiographical memories through audio-visual generation guided by affect extracted from electroencephalogram (EEG) signals.
FCC: Fully Connected Correlation for Few-Shot Segmentation
Therefore, having strong prior information for the target object using the support set is essential for guiding the initial training of FSS, which leads to the success of few-shot segmentation in challenging cases, such as when the target object shows considerable variation in appearance, texture, or scale across the support and query images.
LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking
In this paper, we aim to mitigate such information loss to boost the performance of the low-resolution Transformer tracking via dual knowledge distillation from a frozen high-resolution (but not a larger) Transformer tracker.
Ranked #25 on
Visual Object Tracking
on LaSOT
Efficient Temporal Action Segmentation via Boundary-aware Query Voting
Although the performance of Temporal Action Segmentation (TAS) has improved in recent years, achieving promising results often comes with a high computational cost due to dense inputs, complex model structures, and resource-intensive post-processing requirements.
Ranked #3 on
Action Segmentation
on 50 Salads
VAPO: Visibility-Aware Keypoint Localization for Efficient 6DoF Object Pose Estimation
Since keypoint visibility information is currently missing in the dataset collection process, we propose an efficient way to generate binary visibility labels from available object-level annotations, for keypoints of both asymmetric objects and symmetric objects.
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance.
EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV.
Ranked #1 on
Oriented Object Detection
on EVD4UAV
AesFA: An Aesthetic Feature-Aware Arbitrary Neural Style Transfer
The primary idea is to decompose the image via its frequencies to better disentangle aesthetic styles from the reference image while training the entire model in an end-to-end manner to exclude pre-trained models at inference completely.
Integrated Sensing and Communication Signal Processing Based on Compressed Sensing Over Unlicensed Spectrum Bands
The ISAC-enabled mobile communication system regularly operate in non-continuous spectrum bands due to crowded licensed frequency bands.
Exploring Robust Features for Improving Adversarial Robustness
In this paper, we strive to explore the robust features which are not affected by the adversarial perturbations, i. e., invariant to the clean image and its adversarial examples, to improve the model's adversarial robustness.
INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization
Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.
Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection
Furthermore, we design DefenseNet as a learn-able pre-processing to the adversarial cloudy images so as to preserve the performance of the deep learning based remote sensing SOD model, without tuning the already deployed deep SOD model.
RXFOOD: Plug-in RGB-X Fusion for Object of Interest Detection
The emergence of different sensors (Near-Infrared, Depth, etc.)
Coarse-to-fine Task-driven Inpainting for Geoscience Images
To the best of our knowledge, all the existing image inpainting algorithms learn to repair the occluded regions for a better visualization quality, they are excellent for natural images but not good enough for geoscience images by ignoring the geoscience related tasks.
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning
The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model.
Automated Deepfake Detection
In this paper, we propose to utilize Automated Machine Learning to adaptively search a neural architecture for deepfake detection.
Transparent Object Tracking Benchmark
To the best of our knowledge, TOTB is the first benchmark dedicated to transparent object tracking.
Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition
In this paper, we propose a simple yet effective approach, named Point Adversarial Self Mining (PASM), to improve the recognition accuracy in facial expression recognition.
Interpreting Galaxy Deblender GAN from the Discriminator's Perspective
Using the Galaxy Zoo dataset we demonstrate that our method clearly reveals attention areas of the Discriminator when differentiating generated galaxy images from ground truth images.
Improved Deep Hashing with Soft Pairwise Similarity for Multi-label Image Retrieval
In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the instance similarity is quantified into a percentage based on the normalized semantic labels.
Learning View-Invariant Features for Person Identification in Temporally Synchronized Videos Taken by Wearable Cameras
In this paper, we attempt to utilize 3D human-skeleton sequences to learn a model that can extract view-invariant motion features from optical flows in different views.
Groupwise Tracking of Crowded Similar-Appearance Targets From Low-Continuity Image Sequences
In particular, the within-group association is modeled by a nonrigid 2D Thin-Plate transform and a sequence of group shrinking, group growing and group merging operations are then developed to refine the composition of each group.
Co-interest Person Detection from Multiple Wearable Camera Videos
Wearable cameras, such as Google Glass and Go Pro, enable video data collection over larger areas and from different views.
Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces
By adopting a natural and widely used assumption -- "the data samples from the same class should lay on a low-dimensional subspace, even if they come from different domains", the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the compact joint subspaces of source and target domain.
LooseCut: Interactive Image Segmentation with Loosely Bounded Boxes
In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object.
Combining Local Appearance and Holistic View: Dual-Source Deep Neural Networks for Human Pose Estimation
We propose a new learning-based method for estimating 2D human pose from a single image, using Dual-Source Deep Convolutional Neural Networks (DS-CNN).
Recognize Human Activities from Partially Observed Videos
In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case.
