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Haibin Ling

99 papers hereh-index 9039.8k citations374 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author24
  • last author72

Across the 96 of 99 papers where every author was matched, so the position is known.

fields
  • cs.CV92
  • cs.LG4
  • cs.RO1
  • eess.IV1
  • physics.comp-ph1
same name
  • Haibin Ling — 15 papers, h 2
  • Haibin Ling — 11 papers, h 4
  • Haibin Ling — 9 papers, h 8
  • Haibin Ling — 6 papers, h 4
  • Haibin Ling — 6 papers, h 7
  • Haibin Ling — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152023
most citedDeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection

571 citations · 2.9k across the 98 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022

Local Context-Aware Active Domain Adaptation

Tao Sun, Cheng Lu, Haibin Ling

Active Domain Adaptation (ADA) queries the labels of a small number of selected target samples to help adapting a model from a source domain to a target domain. The local context o…

cs.LG2022★ 4 cited

Domain Adaptation with Adversarial Training on Penultimate Activations

Tao Sun, Cheng Lu, Haibin Ling

Enhancing model prediction confidence on target data is an important objective in Unsupervised Domain Adaptation (UDA). In this paper, we explore adversarial training on penultimat…

cs.LG2022★ 4 cited

Prior Knowledge Guided Unsupervised Domain Adaptation

Tao Sun, Cheng Lu, Haibin Ling

The waive of labels in the target domain makes Unsupervised Domain Adaptation (UDA) an attractive technique in many real-world applications, though it also brings great challenges…

cs.LG2019★ 2 cited

Graph Attribute Aggregation Network with Progressive Margin Folding

Penghui Sun, Jingwei Qu, Xiaoqing Lyu +2

Graph convolutional neural networks (GCNNs) have been attracting increasing research attention due to its great potential in inference over graph structures. However, insufficient…

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