Search Results for author:
Found 7 papers, 4 papers with code
GraphPNAS: Learning Distribution of Good Neural Architectures via Deep Graph Generative Models
Moreover, our graph generator leads to a learnable probabilistic search method that is more flexible and efficient than the commonly used RNN generator and random search methods.
Referring Transformer: A One-step Approach to Multi-task Visual Grounding
As an important step towards visual reasoning, visual grounding (e. g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous approaches to referring expression comprehension (REC) or segmentation (RES) either suffer from limited performance, due to a two-stage setup, or require the designing of complex task-specific one-stage architectures.
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
We present a spatial pixel aggregation network and learn the pixel sampling and averaging strategies for image denoising.
TDAF: Top-Down Attention Framework for Vision Tasks
Such spatial and attention features are nested deeply, therefore, the proposed framework works in a mixed top-down and bottom-up manner.
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
As deep learning brings excellent performances to object detection algorithms, Tracking by Detection (TBD) has become the mainstream tracking framework.
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
For track 2, some existing methods are evaluated, showing promising solutions to the weakly-supervised video quality mapping problem.
Learning Deformable Kernels for Image and Video Denoising
Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input.
