4,254 research outputs found
Higher bottom and bottom-strange mesons
Motivated by the recent observation of the orbital excitation by
CDF collaboration, we have performed a systematical study of the mass spectrum
and strong decay patterns of the higher and mesons. Hopefully the
present investigation may provide valuable clues to further experimental
exploration of these intriguing excited heavy mesons.Comment: 13 pages, 11 figures, 10 tables. More discussions and references
added. Accepted by Phys. Rev.
Semi-Supervised Temporal Action Detection with Proposal-Free Masking
Existing temporal action detection (TAD) methods rely on a large number of
training data with segment-level annotations. Collecting and annotating such a
training set is thus highly expensive and unscalable. Semi-supervised TAD
(SS-TAD) alleviates this problem by leveraging unlabeled videos freely
available at scale. However, SS-TAD is also a much more challenging problem
than supervised TAD, and consequently much under-studied. Prior SS-TAD methods
directly combine an existing proposal-based TAD method and a SSL method. Due to
their sequential localization (e.g, proposal generation) and classification
design, they are prone to proposal error propagation. To overcome this
limitation, in this work we propose a novel Semi-supervised Temporal action
detection model based on PropOsal-free Temporal mask (SPOT) with a parallel
localization (mask generation) and classification architecture. Such a novel
design effectively eliminates the dependence between localization and
classification by cutting off the route for error propagation in-between. We
further introduce an interaction mechanism between classification and
localization for prediction refinement, and a new pretext task for
self-supervised model pre-training. Extensive experiments on two standard
benchmarks show that our SPOT outperforms state-of-the-art alternatives, often
by a large margin. The PyTorch implementation of SPOT is available at
https://github.com/sauradip/SPOTComment: ECCV 2022; Code available at https://github.com/sauradip/SPO
Post-Processing Temporal Action Detection
Existing Temporal Action Detection (TAD) methods typically take a
pre-processing step in converting an input varying-length video into a
fixed-length snippet representation sequence, before temporal boundary
estimation and action classification. This pre-processing step would temporally
downsample the video, reducing the inference resolution and hampering the
detection performance in the original temporal resolution. In essence, this is
due to a temporal quantization error introduced during the resolution
downsampling and recovery. This could negatively impact the TAD performance,
but is largely ignored by existing methods. To address this problem, in this
work we introduce a novel model-agnostic post-processing method without model
redesign and retraining. Specifically, we model the start and end points of
action instances with a Gaussian distribution for enabling temporal boundary
inference at a sub-snippet level. We further introduce an efficient
Taylor-expansion based approximation, dubbed as Gaussian Approximated
Post-processing (GAP). Extensive experiments demonstrate that our GAP can
consistently improve a wide variety of pre-trained off-the-shelf TAD models on
the challenging ActivityNet (+0.2% -0.7% in average mAP) and THUMOS (+0.2%
-0.5% in average mAP) benchmarks. Such performance gains are already
significant and highly comparable to those achieved by novel model designs.
Also, GAP can be integrated with model training for further performance gain.
Importantly, GAP enables lower temporal resolutions for more efficient
inference, facilitating low-resource applications. The code will be available
in https://github.com/sauradip/GAPComment: Technical Repor
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