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Tao Kong

ByteDance Inc.

35 papers hereh-index 3013.5k citations54 works total

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

author position
  • first author4
  • middle author23
  • last author6

Across the 33 of 35 papers where every author was matched, so the position is known.

fields
  • cs.CV24
  • cs.RO8
  • cs.LG2
  • eess.SP1
affiliations
  • ByteDance Inc.
Homepage
same name
  • Tao Kong — 16 papers, h 12
  • Tao Kong — 4 papers, h 2
  • Tao Kong — 1 paper, h 4
  • Tao Kong — 1 paper, 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
20162023
most citedFoveaBox: Beyond Anchor-based Object Detector

940 citations · 2.5k across the 35 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 361 cited

TransTrack: Multiple Object Tracking with Transformer

Peize Sun, Jinkun Cao, Yi Jiang +5

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an at…

cs.CV2020★ 103 cited

Sparse R-CNN: End-to-End Object Detection with Learnable Proposals

Peize Sun, Rufeng Zhang, Yi Jiang +8

We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes…

cs.CV2020★ 40 cited

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Xinlong Wang, Rufeng Zhang, Chunhua Shen +2

To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks…

cs.CV2020★ 480 cited

SOLOv2: Dynamic and Fast Instance Segmentation

Xinlong Wang, Rufeng Zhang, Tao Kong +2

In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance. We follow the principle of the SOLO method of Wang et al. "SOLO…

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