1,268 research outputs found
Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network
Few-shot semantic segmentation (FSS) aims to achieve novel objects
segmentation with only a few annotated samples and has made great progress
recently. Most of the existing FSS models focus on the feature matching between
support and query to tackle FSS. However, the appearance variations between
objects from the same category could be extremely large, leading to unreliable
feature matching and query mask prediction. To this end, we propose a
Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate
latent context structure in query images. Specifically, we propose a
Support-induced Graph Reasoning (SiGR) module to capture salient query object
parts at different semantic levels with a Support-induced GCN. Furthermore, an
instance association (IA) module is designed to capture high-order instance
context from both support and query instances. By integrating the proposed two
modules, SiGCN can learn rich query context representation, and thus being more
robust to appearance variations. Extensive experiments on PASCAL-5i and
COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.Comment: Accepted in BMVC2022 as oral presentatio
PoseTrack: A Benchmark for Human Pose Estimation and Tracking
Human poses and motions are important cues for analysis of videos with people
and there is strong evidence that representations based on body pose are highly
effective for a variety of tasks such as activity recognition, content
retrieval and social signal processing. In this work, we aim to further advance
the state of the art by establishing "PoseTrack", a new large-scale benchmark
for video-based human pose estimation and articulated tracking, and bringing
together the community of researchers working on visual human analysis. The
benchmark encompasses three competition tracks focusing on i) single-frame
multi-person pose estimation, ii) multi-person pose estimation in videos, and
iii) multi-person articulated tracking. To facilitate the benchmark and
challenge we collect, annotate and release a new %large-scale benchmark dataset
that features videos with multiple people labeled with person tracks and
articulated pose. A centralized evaluation server is provided to allow
participants to evaluate on a held-out test set. We envision that the proposed
benchmark will stimulate productive research both by providing a large and
representative training dataset as well as providing a platform to objectively
evaluate and compare the proposed methods. The benchmark is freely accessible
at https://posetrack.net.Comment: www.posetrack.ne
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