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Jia Li

17 papers hereh-index 357k citations76 works total

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

author position
  • first author3
  • middle author9
  • last author5

Across the 17 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CV16
  • cs.LG1
same name
  • Jia Li — 35 papers, h 16
  • Jia Li — 31 papers, h 28
  • Jia Li — 22 papers, h 5
  • Jia Li — 21 papers, h 8
  • Jia Li — 17 papers, h 3
  • Jia Li — 16 papers, h 13

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
20142024
most citedSalient Object Detection: A Benchmark

1.4k citations · 2.2k across the 17 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.CV2018★ 20 cited

Selectivity or Invariance: Boundary-aware Salient Object Detection

Jinming Su, Jia Li, Yu Zhang +2

Typically, a salient object detection (SOD) model faces opposite requirements in processing object interiors and boundaries. The features of interiors should be invariant to strong…

cs.CV2018

Visual Attention on the Sun: What Do Existing Models Actually Predict?

Jia Li, Daowei Li, Kui Fu +1

Visual attention prediction is a classic problem that seems to be well addressed in the deep learning era. One compelling concern, however, gradually arise along with the rapidly g…

cs.CV2018★ 3 cited

Learning from Large-scale Noisy Web Data with Ubiquitous Reweighting for Image Classification

Jia Li, Yafei Song, Jianfeng Zhu +5

Many advances of deep learning techniques originate from the efforts of addressing the image classification task on large-scale datasets. However, the construction of such clean da…

cs.CV2018

Collaborative Annotation of Semantic Objects in Images with Multi-granularity Supervisions

Lishi Zhang, Chenghan Fu, Jia Li

Per-pixel masks of semantic objects are very useful in many applications, which, however, are tedious to be annotated. In this paper, we propose a human-agent collaborative annotat…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.