In this paper, we propose Domain Agnostic Meta Score-based Learning (DAMSL), a novel, versatile and highly effective solution that delivers significant out-performance over state-of-the-art methods for cross-domain few-shot learning.
Ranked #1 on Cross-Domain Few-Shot on miniImagenet
The proposed SUDA features an attention mask mechanism to learn the interaction between the speaker and utterance information streams.
Pedestrian attribute recognition is an important multi-label classification problem.
To address this issue, we propose a novel hard thresholding algorithm, called Semi-stochastic Block Coordinate Descent Hard Thresholding Pursuit (SBCD-HTP).
Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.
In order to solve this problem, majority of existing methods utilize complicated network or bigger image size, which generally leads to higher computation cost.
1 code implementation • 2 Sep 2018 • Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, ShengMei Shen, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
Benchmarking our model on one of the most popular unconstrained face recognition datasets IJB-C additionally verifies the promising generalizability of AIM in recognizing faces in the wild.
Ranked #1 on Age-Invariant Face Recognition on MORPH Album2
To this end, we propose a Pose Invariant Model (PIM) for face recognition in the wild, with three distinct novelties.
Person re-identification (re-ID) aims to accurately re- trieve a person from a large-scale database of images cap- tured across multiple cameras.
How can a delivery robot navigate reliably to a destination in a new office building, with minimal prior information?
In this paper, we propose a unified learning framework named Transferred Deep Feature Fusion (TDFF) targeting at the new IARPA Janus Benchmark A (IJB-A) face recognition dataset released by NIST face challenge.
Taking pedestrian detection as an example, we illustrate how we can leverage this philosophy to develop a Scale-Aware Fast R-CNN (SAF R-CNN) framework.
Ranked #21 on Pedestrian Detection on Caltech