354 research outputs found
Remedies for Patent Infringement: A Comparative Study of U.S. and Chinese Law, 1 J. Marshall Rev. Intell. Prop. L. 35 (2001)
Compared with the long history of U.S. patent law, Chinese patent law is still in its infancy. Nevertheless, there are similarities between the two laws in terms of remedies available for patent infringement. Both provide injunctive relief, damages and provisional rights remedies. Nevertheless, in granting each remedy, there are some differences. China has made consistent efforts to upgrade its patent laws to provide patent owners with adequate remedies. However there is still large room for improvement in the standards for granting preliminary injunctions, and in determining lost profits and reasonable royalties. Additionally, the Supreme Court of China should reconsider the issues of limitations on damages and attorney fees in order to balance the interests of the patent owners and innocent infringers. Unquestionably, the theories and experiences provided by U.S. patent law are good “prior art.
OVSNet : Towards One-Pass Real-Time Video Object Segmentation
Video object segmentation aims at accurately segmenting the target object
regions across consecutive frames. It is technically challenging for coping
with complicated factors (e.g., shape deformations, occlusion and out of the
lens). Recent approaches have largely solved them by using backforth
re-identification and bi-directional mask propagation. However, their methods
are extremely slow and only support offline inference, which in principle
cannot be applied in real time. Motivated by this observation, we propose a
efficient detection-based paradigm for video object segmentation. We propose an
unified One-Pass Video Segmentation framework (OVS-Net) for modeling
spatial-temporal representation in a unified pipeline, which seamlessly
integrates object detection, object segmentation, and object re-identification.
The proposed framework lends itself to one-pass inference that effectively and
efficiently performs video object segmentation. Moreover, we propose a
maskguided attention module for modeling the multi-scale object boundary and
multi-level feature fusion. Experiments on the challenging DAVIS 2017
demonstrate the effectiveness of the proposed framework with comparable
performance to the state-of-the-art, and the great efficiency about 11.5 FPS
towards pioneering real-time work to our knowledge, more than 5 times faster
than other state-of-the-art methods.Comment: 10 pages, 6 figure
Auto-tune: PAC-Bayes Optimization over Prior and Posterior for Neural Networks
It is widely recognized that the generalization ability of neural networks
can be greatly enhanced through carefully designing the training procedure. The
current state-of-the-art training approach involves utilizing stochastic
gradient descent (SGD) or Adam optimization algorithms along with a combination
of additional regularization techniques such as weight decay, dropout, or noise
injection. Optimal generalization can only be achieved by tuning a multitude of
hyperparameters through grid search, which can be time-consuming and
necessitates additional validation datasets. To address this issue, we
introduce a practical PAC-Bayes training framework that is nearly tuning-free
and requires no additional regularization while achieving comparable testing
performance to that of SGD/Adam after a complete grid search and with extra
regularizations. Our proposed algorithm demonstrates the remarkable potential
of PAC training to achieve state-of-the-art performance on deep neural networks
with enhanced robustness and interpretability.Comment: 30 pages, 15 figures, 7 table
Graph Based Long-Term And Short-Term Interest Model for Click-Through Rate Prediction
Click-through rate (CTR) prediction aims to predict the probability that the
user will click an item, which has been one of the key tasks in online
recommender and advertising systems. In such systems, rich user behavior (viz.
long- and short-term) has been proved to be of great value in capturing user
interests. Both industry and academy have paid much attention to this topic and
propose different approaches to modeling with long-term and short-term user
behavior data. But there are still some unresolved issues. More specially, (1)
rule and truncation based methods to extract information from long-term
behavior are easy to cause information loss, and (2) single feedback behavior
regardless of scenario to extract information from short-term behavior lead to
information confusion and noise. To fill this gap, we propose a Graph based
Long-term and Short-term interest Model, termed GLSM. It consists of a
multi-interest graph structure for capturing long-term user behavior, a
multi-scenario heterogeneous sequence model for modeling short-term
information, then an adaptive fusion mechanism to fused information from
long-term and short-term behaviors. Comprehensive experiments on real-world
datasets, GLSM achieved SOTA score on offline metrics. At the same time, the
GLSM algorithm has been deployed in our industrial application, bringing 4.9%
CTR and 4.3% GMV lift, which is significant to the business.Comment: CIKM 2022 accepte
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