Semantic communication aims to transmit meaningful and effective information
rather than focusing on individual symbols or bits, resulting in benefits like
reduced latency, bandwidth usage, and higher throughput compared to traditional
communication. However, semantic communication poses significant challenges due
to the need for universal metrics for benchmarking the joint effects of
semantic information loss and practical energy consumption. This research
presents a novel multi-objective loss function named "Energy-Optimized Semantic
Loss" (EOSL), addressing the challenge of balancing semantic information loss
and energy consumption. Through comprehensive experiments on transformer
models, including CPU and GPU energy usage, it is demonstrated that EOSL-based
encoder model selection can save up to 90\% of energy while achieving a 44\%
improvement in semantic similarity performance during inference in this
experiment. This work paves the way for energy-efficient neural network
selection and the development of greener semantic communication architectures.

语义通信是传输有意义和有效信息的目标，通过 “Energy-Optimized Semantic Loss”（EOSL）这种新型多目标损失函数，旨在解决平衡语义信息丢失和能量消耗的挑战，并通过基于 EOSL 的编码器模型选择在推理过程中实现了 90% 的能量节省和 44% 的语义相似性性能提升，为能源高效的神经网络选择和绿色的语义通信架构发展铺平了道路。

转变为绿色语义通信：更少能量，更多语义

Transformers for Green Semantic Communication: Less Energy, More  Semantics

We present a novel end-to-end framework named as GSNet (Geometric and
Scene-aware Network), which jointly estimates 6DoF poses and reconstructs
detailed 3D car shapes from single urban street view. GSNet utilizes a unique
four-way feature extraction and fusion scheme and directly regresses 6DoF poses
and shapes in a single forward pass. Extensive experiments show that our
diverse feature extraction and fusion scheme can greatly improve model
performance. Based on a divide-and-conquer 3D shape representation strategy,
GSNet reconstructs 3D vehicle shape with great detail (1352 vertices and 2700
faces). This dense mesh representation further leads us to consider geometrical
consistency and scene context, and inspires a new multi-objective loss function
to regularize network training, which in turn improves the accuracy of 6D pose
estimation and validates the merit of jointly performing both tasks. We
evaluate GSNet on the largest multi-task ApolloCar3D benchmark and achieve
state-of-the-art performance both quantitatively and qualitatively. Project
page is available at this https URL

GSNet 采用独特的四向特征提取和融合方案，直接回归 6DoF 姿态和形状，实现从单眼图像中重建具有细节的三维车形。

GSNet: 结合几何和场景感知监督进行车辆姿态和形状重建

GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and  Scene-aware Supervision

We propose novel neural temporal models for predicting and synthesizing human
motion, achieving state-of-the-art in modeling long-term motion trajectories
while being competitive with prior work in short-term prediction and requiring
significantly less computation. Key aspects of our proposed system include: 1)
a novel, two-level processing architecture that aids in generating planned
trajectories, 2) a simple set of easily computable features that integrate
derivative information, and 3) a novel multi-objective loss function that helps
the model to slowly progress from simple next-step prediction to the harder
task of multi-step, closed-loop prediction. Our results demonstrate that these
innovations improve the modeling of long-term motion trajectories. Finally, we
propose a novel metric, called Normalized Power Spectrum Similarity (NPSS), to
evaluate the long-term predictive ability of motion synthesis models,
complementing the popular mean-squared error (MSE) measure of Euler joint
angles over time. We conduct a user study to determine if the proposed NPSS
correlates with human evaluation of long-term motion more strongly than MSE and
find that it indeed does. We release code and additional results
(visualizations) for this paper at:
this https URL

提出了一种新的神经时间模型来预测和合成人类动作，并且在长期运动轨迹的建模方面达到了最先进水平，同时在短期预测方面具有竞争力并且需要更少的计算量，通过一种新颖的指标（NPSS）评估长期预测能力，配合用户研究，成功提高了模型的预测准确度。