252 research outputs found
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Discourse right and participation: using application for hunting permits for foreigners at Dulan International Hunting Ground as a case study
Heterogeneous Forgetting Compensation for Class-Incremental Learning
Class-incremental learning (CIL) has achieved remarkable successes in
learning new classes consecutively while overcoming catastrophic forgetting on
old categories. However, most existing CIL methods unreasonably assume that all
old categories have the same forgetting pace, and neglect negative influence of
forgetting heterogeneity among different old classes on forgetting
compensation. To surmount the above challenges, we develop a novel
Heterogeneous Forgetting Compensation (HFC) model, which can resolve
heterogeneous forgetting of easy-to-forget and hard-to-forget old categories
from both representation and gradient aspects. Specifically, we design a
task-semantic aggregation block to alleviate heterogeneous forgetting from
representation aspect. It aggregates local category information within each
task to learn task-shared global representations. Moreover, we develop two
novel plug-and-play losses: a gradient-balanced forgetting compensation loss
and a gradient-balanced relation distillation loss to alleviate forgetting from
gradient aspect. They consider gradient-balanced compensation to rectify
forgetting heterogeneity of old categories and heterogeneous relation
consistency. Experiments on several representative datasets illustrate
effectiveness of our HFC model. The code is available at
https://github.com/JiahuaDong/HFC.Comment: Accepted to ICCV202
Create Your World: Lifelong Text-to-Image Diffusion
Text-to-image generative models can produce diverse high-quality images of
concepts with a text prompt, which have demonstrated excellent ability in image
generation, image translation, etc. We in this work study the problem of
synthesizing instantiations of a use's own concepts in a never-ending manner,
i.e., create your world, where the new concepts from user are quickly learned
with a few examples. To achieve this goal, we propose a Lifelong text-to-image
Diffusion Model (L2DM), which intends to overcome knowledge "catastrophic
forgetting" for the past encountered concepts, and semantic "catastrophic
neglecting" for one or more concepts in the text prompt. In respect of
knowledge "catastrophic forgetting", our L2DM framework devises a task-aware
memory enhancement module and a elastic-concept distillation module, which
could respectively safeguard the knowledge of both prior concepts and each past
personalized concept. When generating images with a user text prompt, the
solution to semantic "catastrophic neglecting" is that a concept attention
artist module can alleviate the semantic neglecting from concept aspect, and an
orthogonal attention module can reduce the semantic binding from attribute
aspect. To the end, our model can generate more faithful image across a range
of continual text prompts in terms of both qualitative and quantitative
metrics, when comparing with the related state-of-the-art models. The code will
be released at https://wenqiliang.github.io/.Comment: 15 pages,10 figure
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters
Retouching images is an essential aspect of enhancing the visual appeal of
photos. Although users often share common aesthetic preferences, their
retouching methods may vary based on their individual preferences. Therefore,
there is a need for white-box approaches that produce satisfying results and
enable users to conveniently edit their images simultaneously. Recent white-box
retouching methods rely on cascaded global filters that provide image-level
filter arguments but cannot perform fine-grained retouching. In contrast,
colorists typically employ a divide-and-conquer approach, performing a series
of region-specific fine-grained enhancements when using traditional tools like
Davinci Resolve. We draw on this insight to develop a white-box framework for
photo retouching using parallel region-specific filters, called RSFNet. Our
model generates filter arguments (e.g., saturation, contrast, hue) and
attention maps of regions for each filter simultaneously. Instead of cascading
filters, RSFNet employs linear summations of filters, allowing for a more
diverse range of filter classes that can be trained more easily. Our
experiments demonstrate that RSFNet achieves state-of-the-art results, offering
satisfying aesthetic appeal and increased user convenience for editable
white-box retouching.Comment: Accepted by ICCV 202
Are incomplete and self-confident preference relations better in multicriteria decision making? A simulation-based investigation
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Incomplete preference relations and self-confident preference relations have been widely used in multicriteria decision-making problems. However, there is no strong evidence, in the current literature, to validate their use in decision-making. This paper reports on the design of two bounded rationality principle based simulation methods, and detailed experimental results, that aim at providing evidence to answer the following two questions: (1) what are the conditions under which incomplete preference relations are better than complete preference relations?; and (2) can self-confident preference relations improve the quality of decisions? The experimental results show that when the decision-maker is of medium rational degree, incomplete preference relations with a degree of incompleteness between 20% and 40% outperform complete preference relations; otherwise, the opposite happens. Furthermore, in most cases the quality of the decision making improves when using self-confident preference relations instead of incomplete preference relations. The paper ends with the presentation of a sensitivity analysis that contributes to the robustness of the experimental conclusions
A New Load Torque Identification Sliding Mode Observer for Permanent Magnet Synchronous Machine Drive System
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