Search Results for author:
Found 21 papers, 6 papers with code
CLIFFNet for Monocular Depth Estimation with Hierarchical Embedding Loss
This paper proposes a hierarchical loss for monocular depth estimation, which measures the differences between the prediction and ground truth in hierarchical embedding spaces of depth maps.
Blind estimation of room acoustic parameters from speech signals based on extended model of room impulse response
The root-mean-square errors between the estimated and ground-truth results were used to comparatively evaluate the proposed method with the previous method.
Multi-Source Uncertainty Mining for Deep Unsupervised Saliency Detection
We design an Uncertainty Mining Network (UMNet) which consists of multiple Merge-and-Split (MS) modules to recursively analyze the commonality and difference among multiple noisy labels and infer pixel-wise uncertainty map for each label.
Comparison between Time Shifting Deviation and Cross-correlation Methods
It stands in clear contrast to the result of cross-correlation method, whose localization error is 70 m and the standard deviation is 208. 4 m. Compared with cross-correlation method, TSDEV has the same resistance to white noise, but has fewer boundary conditions and better suppression on linear drift or common noise, which leads to more precise TDE results.
Towards Toxic and Narcotic Medication Detection with Rotated Object Detector
Recent years have witnessed the advancement of deep learning vision technologies and applications in the medical industry.
Video Annotation for Visual Tracking via Selection and Refinement
Deep learning based visual trackers entail offline pre-training on large volumes of video datasets with accurate bounding box annotations that are labor-expensive to achieve.
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?
The integration of SAG loss and two-stream network enables more consistent scale inference and more accurate relative depth estimation.
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Spherical videos, also known as \ang{360} (panorama) videos, can be viewed with various virtual reality devices such as computers and head-mounted displays.
Appearance-free Tripartite Matching for Multiple Object Tracking
We focus on the general MOT problem regardless of the appearance and propose an appearance-free tripartite matching to avoid the irregular velocity problem of the bipartite matching.
SDC-Depth: Semantic Divide-and-Conquer Network for Monocular Depth Estimation
Monocular depth estimation is an ill-posed problem, and as such critically relies on scene priors and semantics.
Ranked #1 on
Depth Estimation
on Cityscapes test
When Relation Networks meet GANs: Relation GANs with Triplet Loss
Though recent research has achieved remarkable progress in generating realistic images with generative adversarial networks (GANs), the lack of training stability is still a lingering concern of most GANs, especially on high-resolution inputs and complex datasets.
FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint Comparison
We designed a fast similarity search engine for large molecular libraries: FPScreen.
DeepLens: Shallow Depth Of Field From A Single Image
To achieve this, we propose a novel neural network model comprised of a depth prediction module, a lens blur module, and a guided upsampling module.
Learning regression and verification networks for long-term visual tracking
Compared with short-term tracking, the long-term tracking task requires determining the tracked object is present or absent, and then estimating the accurate bounding box if present or conducting image-wide re-detection if absent.
Structured Siamese Network for Real-Time Visual Tracking
In this paper, we circumvent this issue by proposing a local structure learning method, which simultaneously considers the local patterns of the target and their structural relationships for more accurate target tracking.
Learning to Detect Salient Objects With Image-Level Supervision
In the second stage, FIN is fine-tuned with its predicted saliency maps as ground truth.
Visual Tracking via Shallow and Deep Collaborative Model
In the discriminative model, we exploit the advances of deep learning architectures to learn generic features which are robust to both background clutters and foreground appearance variations.
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
In this paper, we seek an alternative and propose a new image SR method, which jointly learns the feature extraction, upsampling and HR reconstruction modules, yielding a completely end-to-end trainable deep CNN.
STCT: Sequentially Training Convolutional Networks for Visual Tracking
To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features.
Visual Tracking With Fully Convolutional Networks
Instead of treating convolutional neural network (CNN) as a black-box feature extractor, we conduct in-depth study on the properties of CNN features offline pre-trained on massive image data and classification task on ImageNet.
Deep Networks for Saliency Detection via Local Estimation and Global Search
In the global search stage, the local saliency map together with global contrast and geometric information are used as global features to describe a set of object candidate regions.
