25 citations · 34 across the 5 of their papers we have counts for
10 papers · 1 filter
Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting
Min Shi, Hao Lu, Chen Feng +2
Class-agnostic counting (CAC) aims to count all instances in a query image given few exemplars. A standard pipeline is to extract visual features from exemplars and match them with…
Learning Affinity-Aware Upsampling for Deep Image Matting
Yutong Dai, Hao Lu, Chunhua Shen
We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used…
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning
Liang Liu, Hao Lu, Hongwei Zou +3
We formulate counting as a sequential decision problem and present a novel crowd counting model solvable by deep reinforcement learning. In contrast to existing counting models tha…
From Open Set to Closed Set: Supervised Spatial Divide-and-Conquer for Object Counting
Haipeng Xiong, Hao Lu, Chengxin Liu +3
Visual counting, a task that aims to estimate the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in [0, inf) in th…
From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer
Haipeng Xiong, Hao Lu, Chengxin Liu +3
Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in in theor…
Indices Matter: Learning to Index for Deep Image Matting
Hao Lu, Yutong Dai, Chunhua Shen +1
We show that existing upsampling operators can be unified with the notion of the index function. This notion is inspired by an observation in the decoding process of deep image mat…