Search Results for author:
Found 9 papers, 3 papers with code
From Shadow Generation to Shadow Removal
Shadow removal is a computer-vision task that aims to restore the image content in shadow regions.
Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data
In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data.
A DoA Estimation Based Robust Beam Forming Method for UAV-BS Communication
Then, we propose a DoA estimation algorithm and a steering vector adaptive receiving beam forming method.
Understanding and Visualizing Deep Visual Saliency Models
Our analyses reveal that: 1) some visual regions (e. g. head, text, symbol, vehicle) are already encoded within various layers of the network pre-trained for object recognition, 2) using modern datasets, we find that fine-tuning pre-trained models for saliency prediction makes them favor some categories (e. g. head) over some others (e. g. text), 3) although deep models of saliency outperform classical models on natural images, the converse is true for synthetic stimuli (e. g. pop-out search arrays), an evidence of significant difference between human and data-driven saliency models, and 4) we confirm that, after-fine tuning, the change in inner-representations is mostly due to the task and not the domain shift in the data.
Aggregated Sparse Attention for Steering Angle Prediction
In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction.
What Catches the Eye? Visualizing and Understanding Deep Saliency Models
Deep convolutional neural networks have demonstrated high performances for fixation prediction in recent years.
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion
In this paper, we propose a novel deep-based framework for action recognition, which improves the recognition accuracy by: 1) deriving more precise features for representing actions, and 2) reducing the asynchrony between different information streams.
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.
A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes
These semantic regions can be used to recognize pre-defined activities in crowd scenes.
