Search Results for author:
Found 23 papers, 9 papers with code
Co-Fix3D: Enhancing 3D Object Detection with Collaborative Refinement
Our method incorporates the Local-Global Feature Enhancement (LGE) module, which refines BEV features to more effectively highlight weak positive samples.
ED$^4$: Explicit Data-level Debiasing for Deepfake Detection
Learning intrinsic bias from limited data has been considered the main reason for the failure of deepfake detection with generalizability.
IDRetracor: Towards Visual Forensics Against Malicious Face Swapping
To achieve visual forensics and target face attribution, we propose a novel task named face retracing, which considers retracing the original target face from the given fake one via inverse mapping.
NePF: Neural Photon Field for Single-Stage Inverse Rendering
Contrary to previous methods that recover the geometry, material, and illumination in multiple stages and extract the properties from various multi-layer perceptrons across different neural fields, we question such complexities and introduce our method - a single-stage framework that uniformly recovers all properties.
Domain Adaptation based Object Detection for Autonomous Driving in Foggy and Rainy Weather
To bridge the domain gap and improve the performance of object detection in foggy and rainy weather, this paper presents a novel framework for domain-adaptive object detection.
S2R-ViT for Multi-Agent Cooperative Perception: Bridging the Gap from Simulation to Reality
We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Deployment Gap and an agent-based feature adaptation module with inter-agent and ego-agent discriminators to reduce the Feature Gap.
Implicit Identity Driven Deepfake Face Swapping Detection
Face swapping aims to replace the target face with the source face and generate the fake face that the human cannot distinguish between real and fake.
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.
Line Drawing Guided Progressive Inpainting of Mural Damage
The proposed approach is evaluated against the current state-of-the-art image inpainting methods.
Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather
This paper proposes a novel domain adaptive object detection framework for autonomous driving under foggy weather.
Luminance Attentive Networks for HDR Image and Panorama Reconstruction
Based on this observation, we propose a novel normalization method called " HDR calibration " for HDR images stored in relative luminance, calibrating HDR images into a similar luminance scale according to the LDR images.
Metric Learning for Anti-Compression Facial Forgery Detection
Detecting facial forgery images and videos is an increasingly important topic in multimedia forensics.
Transductive Zero-Shot Hashing for Multilabel Image Retrieval
Given semantic annotations such as class labels and pairwise similarities of the training data, hashing methods can learn and generate effective and compact binary codes.
An End-to-End Network for Co-Saliency Detection in One Single Image
Co-saliency detection within a single image is a common vision problem that has received little attention and has not yet been well addressed.
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer
Given the fact that one same activity may be represented by videos in both high resolution (HR) and extreme low resolution (eLR), it is worth studying to utilize the relevant HR data to improve the eLR activity recognition.
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction.
Privacy-Preserving Collaborative Deep Learning with Unreliable Participants
With powerful parallel computing GPUs and massive user data, neural-network-based deep learning can well exert its strong power in problem modeling and solving, and has archived great success in many applications such as image classification, speech recognition and machine translation etc.
Deep Learning-Based Gait Recognition Using Smartphones in the Wild
Specifically, a hybrid deep neural network is proposed for robust gait feature representation, where features in the space and time domains are successively abstracted by a convolutional neural network and a recurrent neural network.
Dating Ancient Paintings of Mogao Grottoes Using Deeply Learnt Visual Codes
Cultural heritage is the asset of all the peoples of the world.
Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification
The clothing fashion reflects the common aesthetics that people share with each other in dressing.
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.
Robust Gait Recognition by Integrating Inertial and RGBD Sensors
We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively.
Who Leads the Clothing Fashion: Style, Color, or Texture? A Computational Study
Specifically, a classification-based model is proposed to quantify the influence of different visual stimuli, in which each visual stimulus's influence is quantified by its corresponding accuracy in fashion classification.
