Search Results for author:
Found 11 papers, 2 papers with code
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images.
Ranked #2 on
Surface Normals Estimation
on Stanford-ORB
Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors
In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface.
Open World DETR: Transformer based Open World Object Detection
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision.
Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.
Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection
In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task.
Performance Analysis and Optimization of Cooperative Satellite-Aerial-Terrestrial Systems
Aerial relays have been regarded as an alternative and promising solution to extend and improve satellite-terrestrial communications, as the probability of line-of-sight transmissions increases compared with adopting terrestrial relays.
SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network
In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection.
W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection
Finally, we use these pseudo ground-truths to train a fully-supervised detector.
Finding Tiny Faces in the Wild With Generative Adversarial Network
In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN).
Low-Rank-Sparse Subspace Representation for Robust Regression
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications.
