Search Results for author:
Found 22 papers, 5 papers with code
ReFace: Improving Clothes-Changing Re-Identification With Face Features
Person re-identification (ReID) has been an active research field for many years.
Ranked #1 on
Person Re-Identification
on PRCC
Deep Audio Waveform Prior
A network with relevant deep priors is likely to generate a cleaner version of the signal before converging on the corrupted signal.
A Peek at Peak Emotion Recognition
Despite much progress in the field of facial expression recognition, little attention has been paid to the recognition of peak emotion.
Membership Inference Attacks are Easier on Difficult Problems
Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e. g. to detect copyright abuses.
Membership Attacks on Conditional Generative Models Using Image Difficulty
A simple but effective approach for membership attacks can therefore use the reconstruction error.
Crypto-Oriented Neural Architecture Design
As neural networks revolutionize many applications, significant privacy conflicts between model users and providers emerge.
Dynamic Temporal Alignment of Speech to Lips
This alignment is based on deep audio-visual features, mapping the lips video and the speech signal to a shared representation.
Visual Speech Enhancement
When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise.
Seeing Through Noise: Visually Driven Speaker Separation and Enhancement
Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments.
Improved Speech Reconstruction from Silent Video
Speechreading is the task of inferring phonetic information from visually observed articulatory facial movements, and is a notoriously difficult task for humans to perform.
Vid2speech: Speech Reconstruction from Silent Video
Speechreading is a notoriously difficult task for humans to perform.
Fundamental Matrices from Moving Objects Using Line Motion Barcodes
Corresponding epipolar lines have similar motion barcodes, and candidate pairs of corresponding epipoar lines are found by the similarity of their motion barcodes.
EgoSampling: Wide View Hyperlapse from Egocentric Videos
However, this accentuates the shake caused by natural head motion in an egocentric video, making the fast forwarded video useless.
Epipolar Geometry Based On Line Similarity
This paper proposes a similarity measure between lines that indicates whether two lines are corresponding epipolar lines and enables finding epipolar line correspondences as needed for the computation of epipolar geometry.
Camera Calibration from Dynamic Silhouettes Using Motion Barcodes
The use of motion barcodes leads to increased speed, accuracy, and robustness in computing the epipolar geometry.
Visual Learning of Arithmetic Operations
This indicates that while some tasks may be easily learnable end-to-end, other may need to be broken into sub-tasks.
Live Video Synopsis for Multiple Cameras
Video surveillance cameras generate most of recorded video, and there is far more recorded video than operators can watch.
Compact CNN for Indexing Egocentric Videos
Furthermore, our CNN is able to recognize whether a video is egocentric or not with 99. 2% accuracy, up by 24% from current state-of-the-art.
EgoSampling: Fast-Forward and Stereo for Egocentric Videos
While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end.
Event Retrieval Using Motion Barcodes
We introduce a simple and effective method for retrieval of videos showing a specific event, even when the videos of that event were captured from significantly different viewpoints.
An Egocentric Look at Video Photographer Identity
As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed.
Temporal Segmentation of Egocentric Videos
Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video.
