928 research outputs found
Investigating Bottom-Quark Yukawa Interaction at Higgs Factory
Measuring the fermion Yukawa coupling constants is important for
understanding the origin of the fermion masses and its relationship to the
spontaneously electroweak symmetry breaking. On the other hand, some new
physics models will change the Lorentz structure of the Yukawa interactions
between the standard model (SM) fermions and the SM-like Higgs boson even in
their decoupling limit. Thus the precisely measurement of the fermion Yukawa
interactions is a powerful tool of new physics searching in the decoupling
limit. In this work, we show the possibility of investigating the Lorentz
structure of the bottom-quark Yukawa interaction with the 125GeV SM-like Higgs
boson at future colliders.Comment: 8 pages, 7 figure
Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles
We propose a perception imitation method to simulate results of a certain
perception model, and discuss a new heuristic route of autonomous driving
simulator without data synthesis. The motivation is that original sensor data
is not always necessary for tasks such as planning and control when semantic
perception results are ready, so that simulating perception directly is more
economic and efficient. In this work, a series of evaluation methods such as
matching metric and performance of downstream task are exploited to examine the
simulation quality. Experiments show that our method is effective to model the
behavior of learning-based perception model, and can be further applied in the
proposed simulation route smoothly
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation
Panoptic segmentation that unifies instance segmentation and semantic
segmentation has recently attracted increasing attention. While most existing
methods focus on designing novel architectures, we steer toward a different
perspective: performing automated multi-loss adaptation (named Ada-Segment) on
the fly to flexibly adjust multiple training losses over the course of training
using a controller trained to capture the learning dynamics. This offers a few
advantages: it bypasses manual tuning of the sensitive loss combination, a
decisive factor for panoptic segmentation; it allows to explicitly model the
learning dynamics, and reconcile the learning of multiple objectives (up to ten
in our experiments); with an end-to-end architecture, it generalizes to
different datasets without the need of re-tuning hyperparameters or
re-adjusting the training process laboriously. Our Ada-Segment brings 2.7%
panoptic quality (PQ) improvement on COCO val split from the vanilla baseline,
achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on
ADE20K dataset. The extensive ablation studies reveal the ever-changing
dynamics throughout the training process, necessitating the incorporation of an
automated and adaptive learning strategy as presented in this paper.Comment: Accepted by AAAI202
RoSI: Recovering 3D Shape Interiors from Few Articulation Images
The dominant majority of 3D models that appear in gaming, VR/AR, and those we
use to train geometric deep learning algorithms are incomplete, since they are
modeled as surface meshes and missing their interior structures. We present a
learning framework to recover the shape interiors (RoSI) of existing 3D models
with only their exteriors from multi-view and multi-articulation images. Given
a set of RGB images that capture a target 3D object in different articulated
poses, possibly from only few views, our method infers the interior planes that
are observable in the input images. Our neural architecture is trained in a
category-agnostic manner and it consists of a motion-aware multi-view analysis
phase including pose, depth, and motion estimations, followed by interior plane
detection in images and 3D space, and finally multi-view plane fusion. In
addition, our method also predicts part articulations and is able to realize
and even extrapolate the captured motions on the target 3D object. We evaluate
our method by quantitative and qualitative comparisons to baselines and
alternative solutions, as well as testing on untrained object categories and
real image inputs to assess its generalization capabilities
Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis
Recent text-to-image generative models can generate high-fidelity images from
text inputs, but the quality of these generated images cannot be accurately
evaluated by existing evaluation metrics. To address this issue, we introduce
Human Preference Dataset v2 (HPD v2), a large-scale dataset that captures human
preferences on images from a wide range of sources. HPD v2 comprises 798,090
human preference choices on 433,760 pairs of images, making it the largest
dataset of its kind. The text prompts and images are deliberately collected to
eliminate potential bias, which is a common issue in previous datasets. By
fine-tuning CLIP on HPD v2, we obtain Human Preference Score v2 (HPS v2), a
scoring model that can more accurately predict human preferences on generated
images. Our experiments demonstrate that HPS v2 generalizes better than
previous metrics across various image distributions and is responsive to
algorithmic improvements of text-to-image generative models, making it a
preferable evaluation metric for these models. We also investigate the design
of the evaluation prompts for text-to-image generative models, to make the
evaluation stable, fair and easy-to-use. Finally, we establish a benchmark for
text-to-image generative models using HPS v2, which includes a set of recent
text-to-image models from the academic, community and industry. The code and
dataset is available at https://github.com/tgxs002/HPSv2 .Comment: Revisio
Contrastive Label Disambiguation for Self-Supervised Terrain Traversability Learning in Off-Road Environments
Discriminating the traversability of terrains is a crucial task for
autonomous driving in off-road environments. However, it is challenging due to
the diverse, ambiguous, and platform-specific nature of off-road
traversability. In this paper, we propose a novel self-supervised terrain
traversability learning framework, utilizing a contrastive label disambiguation
mechanism. Firstly, weakly labeled training samples with pseudo labels are
automatically generated by projecting actual driving experiences onto the
terrain models constructed in real time. Subsequently, a prototype-based
contrastive representation learning method is designed to learn distinguishable
embeddings, facilitating the self-supervised updating of those pseudo labels.
As the iterative interaction between representation learning and pseudo label
updating, the ambiguities in those pseudo labels are gradually eliminated,
enabling the learning of platform-specific and task-specific traversability
without any human-provided annotations. Experimental results on the RELLIS-3D
dataset and our Gobi Desert driving dataset demonstrate the effectiveness of
the proposed method.Comment: 9 pages, 11 figure
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