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Fine-Grained Human-Centric Tracklet Segmentation with Single Frame Supervision

In this paper, we target at the Fine-grAined human-Centric Tracklet Segmentation (FACTS) problem, where 12 human parts, e.g., face, pants, left-leg, are segmented. To reduce the heavy and tedious labeling efforts, FACTS requires only one labeled frame per video during training. The small size of human parts and the labeling scarcity makes FACTS very challenging. Considering adjacent frames of videos are continuous and human usually do not change clothes in a short time, we explicitly consider the pixel-level and frame-level context in the proposed Temporal Context segmentation Network (TCNet). On the one hand, optical flow is on-line calculated to propagate the pixel-level segmentation results to neighboring frames. On the other hand, frame-level classification likelihood vectors are also propagated to nearby frames. By fully exploiting the pixel-level and frame-level context, TCNet indirectly uses the large amount of unlabeled frames during training and produces smooth segmentation results during inference. Experimental results on four video datasets show the superiority of TCNet over the state-of-the-arts. The newly annotated datasets can be downloaded via http://liusi-group.com/projects/FACTS for the further studies.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.2 February 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 610-621

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FINE-GRAINED
VIDEO OBJECT SEGMENTATION
HUMAN-CENTRIC
OPTICAL FLOW ESTIMATION

Info Detail Spesifik

DOI: 10.1109/TPAMI.2019.2911936

Pernyataan Tanggungjawab

Si Liu, Guanghui Ren, Yao Sun, Jinqiao Wang, Changhu Wang, Bo Li, Shuicheng Yan

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