16,048 research outputs found
Fractal analysis of the effect of particle aggregation distribution on thermal conductivity of nanofluids
This project was supported by the National Natural Science Foundation of China (No. 41572116), the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan) (No. CUG160602).Peer reviewedPostprin
Relation Networks for Object Detection
Although it is well believed for years that modeling relations between
objects would help object recognition, there has not been evidence that the
idea is working in the deep learning era. All state-of-the-art object detection
systems still rely on recognizing object instances individually, without
exploiting their relations during learning.
This work proposes an object relation module. It processes a set of objects
simultaneously through interaction between their appearance feature and
geometry, thus allowing modeling of their relations. It is lightweight and
in-place. It does not require additional supervision and is easy to embed in
existing networks. It is shown effective on improving object recognition and
duplicate removal steps in the modern object detection pipeline. It verifies
the efficacy of modeling object relations in CNN based detection. It gives rise
to the first fully end-to-end object detector
Deformable Convolutional Networks
Convolutional neural networks (CNNs) are inherently limited to model
geometric transformations due to the fixed geometric structures in its building
modules. In this work, we introduce two new modules to enhance the
transformation modeling capacity of CNNs, namely, deformable convolution and
deformable RoI pooling. Both are based on the idea of augmenting the spatial
sampling locations in the modules with additional offsets and learning the
offsets from target tasks, without additional supervision. The new modules can
readily replace their plain counterparts in existing CNNs and can be easily
trained end-to-end by standard back-propagation, giving rise to deformable
convolutional networks. Extensive experiments validate the effectiveness of our
approach on sophisticated vision tasks of object detection and semantic
segmentation. The code would be released
Are Large Language Models Good Fact Checkers: A Preliminary Study
Recently, Large Language Models (LLMs) have drawn significant attention due
to their outstanding reasoning capabilities and extensive knowledge repository,
positioning them as superior in handling various natural language processing
tasks compared to other language models. In this paper, we present a
preliminary investigation into the potential of LLMs in fact-checking. This
study aims to comprehensively evaluate various LLMs in tackling specific
fact-checking subtasks, systematically evaluating their capabilities, and
conducting a comparative analysis of their performance against pre-trained and
state-of-the-art low-parameter models. Experiments demonstrate that LLMs
achieve competitive performance compared to other small models in most
scenarios. However, they encounter challenges in effectively handling Chinese
fact verification and the entirety of the fact-checking pipeline due to
language inconsistencies and hallucinations. These findings underscore the need
for further exploration and research to enhance the proficiency of LLMs as
reliable fact-checkers, unveiling the potential capability of LLMs and the
possible challenges in fact-checking tasks
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