114 research outputs found
Romantic Narrative in the Film The Battle at Lake Changjin
Chen Kaige, a famous Chinese “scholar-type director”, usually contains rich cultural connotations in his films and his works are always full of Chen Kaige-style romanticism. Although he is good at narrative, making The Battle of Changjin Lake a pure war film is a challenge. Therefore, this article takes the film as the research focus. The analysis of aesthetic elements, such as scenes and scope, picture composition, color and so on, expounds into detail the narrative style of romanticism displayed by Chen Kaige in the film
Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models
The Segment Anything Model (SAM) exhibits remarkable versatility and
zero-shot learning abilities, owing largely to its extensive training data
(SA-1B). Recognizing SAM's dependency on manual guidance given its
category-agnostic nature, we identified unexplored potential within few-shot
semantic segmentation tasks for remote sensing imagery. This research
introduces a structured framework designed for the automation of few-shot
semantic segmentation. It utilizes the SAM model and facilitates a more
efficient generation of semantically discernible segmentation outcomes. Central
to our methodology is a novel automatic prompt learning approach, leveraging
prior guided masks to produce coarse pixel-wise prompts for SAM. Extensive
experiments on the DLRSD datasets underline the superiority of our approach,
outperforming other available few-shot methodologies
Chinese health funding in Africa: The untold story
The motivations behind China’s allocation of health aid to Africa remain complex due to limited information on the details of health aid project activities. Insufficient knowledge about the purpose of China’s health aid hinders our understanding of China’s comprehensive role in supporting Africa’s healthcare system. To address this gap, our study aimed to gain better insights into China’s health aid priorities and the factors driving these priorities across Africa. To achieve this, we utilized AidData’s Chinese Official Finance Dataset and adhered to the Organisation for Economic Co-operation and Development (OECD) guidelines. We reclassified all 1,026 health projects in Africa, originally categorized under broad 3-digit OECDDAC sector codes, into more specific 5-digit CRS codes. By analyzing the project count and financial value, we assessed the shifting priorities over time. Our analysis revealed that China’s priorities in health aid have evolved between 2000 and 2017. In the early 2000s, China primarily allocated aid to basic health personnel and lacked diversity in sub-sectors. However, after 2004, China shifted its focus more toward basic infrastructure and reduced emphasis on clinical-level staff. Furthermore, China’s interest in addressing malaria expanded both in scale and depth between 2006 and 2009. This trend continued in 2012 and 2014 when China responded to the Ebola outbreak by shifting its focus from basic infrastructure to infectious diseases. In summary, our findings demonstrate the changes in China’s health aid strategy, starting with addressing diseases already eliminated in China and gradually transitioning towards global health security, health system strengthening, and shaping the governance mechanisms
Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
Few-shot segmentation (FSS) is proposed to segment unknown class targets with
just a few annotated samples. Most current FSS methods follow the paradigm of
mining the semantics from the support images to guide the query image
segmentation. However, such a pattern of `learning from others' struggles to
handle the extreme intra-class variation, preventing FSS from being directly
generalized to remote sensing scenes. To bridge the gap of intra-class
variance, we develop a Dual-Mining network named DMNet for cross-image mining
and self-mining, meaning that it no longer focuses solely on support images but
pays more attention to the query image itself. Specifically, we propose a
Class-public Region Mining (CPRM) module to effectively suppress irrelevant
feature pollution by capturing the common semantics between the support-query
image pair. The Class-specific Region Mining (CSRM) module is then proposed to
continuously mine the class-specific semantics of the query image itself in a
`filtering' and `purifying' manner. In addition, to prevent the co-existence of
multiple classes in remote sensing scenes from exacerbating the collapse of FSS
generalization, we also propose a new Known-class Meta Suppressor (KMS) module
to suppress the activation of known-class objects in the sample. Extensive
experiments on the iSAID and LoveDA remote sensing datasets have demonstrated
that our method sets the state-of-the-art with a minimum number of model
parameters. Significantly, our model with the backbone of Resnet-50 achieves
the mIoU of 49.58% and 51.34% on iSAID under 1-shot and 5-shot settings,
outperforming the state-of-the-art method by 1.8% and 1.12%, respectively. The
code is publicly available at https://github.com/HanboBizl/DMNet.Comment: accepted to IEEE TGR
Semantic Segmentation for Point Cloud Scenes via Dilated Graph Feature Aggregation and Pyramid Decoders
Semantic segmentation of point clouds generates comprehensive understanding
of scenes through densely predicting the category for each point. Due to the
unicity of receptive field, semantic segmentation of point clouds remains
challenging for the expression of multi-receptive field features, which brings
about the misclassification of instances with similar spatial structures. In
this paper, we propose a graph convolutional network DGFA-Net rooted in dilated
graph feature aggregation (DGFA), guided by multi-basis aggregation loss
(MALoss) calculated through Pyramid Decoders. To configure multi-receptive
field features, DGFA which takes the proposed dilated graph convolution
(DGConv) as its basic building block, is designed to aggregate multi-scale
feature representation by capturing dilated graphs with various receptive
regions. By simultaneously considering penalizing the receptive field
information with point sets of different resolutions as calculation bases, we
introduce Pyramid Decoders driven by MALoss for the diversity of receptive
field bases. Combining these two aspects, DGFA-Net significantly improves the
segmentation performance of instances with similar spatial structures.
Experiments on S3DIS, ShapeNetPart and Toronto-3D show that DGFA-Net
outperforms the baseline approach, achieving a new state-of-the-art
segmentation performance.Comment: accepted to AAAI Workshop 202
Breaking Immutable: Information-Coupled Prototype Elaboration for Few-Shot Object Detection
Few-shot object detection, expecting detectors to detect novel classes with a
few instances, has made conspicuous progress. However, the prototypes extracted
by existing meta-learning based methods still suffer from insufficient
representative information and lack awareness of query images, which cannot be
adaptively tailored to different query images. Firstly, only the support images
are involved for extracting prototypes, resulting in scarce perceptual
information of query images. Secondly, all pixels of all support images are
treated equally when aggregating features into prototype vectors, thus the
salient objects are overwhelmed by the cluttered background. In this paper, we
propose an Information-Coupled Prototype Elaboration (ICPE) method to generate
specific and representative prototypes for each query image. Concretely, a
conditional information coupling module is introduced to couple information
from the query branch to the support branch, strengthening the query-perceptual
information in support features. Besides, we design a prototype dynamic
aggregation module that dynamically adjusts intra-image and inter-image
aggregation weights to highlight the salient information useful for detecting
query images. Experimental results on both Pascal VOC and MS COCO demonstrate
that our method achieves state-of-the-art performance in almost all settings.Comment: Accepted by AAAI202
Parallel Prediction Method of Knowledge Proficiency Based on Bloom’s Cognitive Theory
Knowledge proficiency refers to the extent to which students master knowledge and reflects their cognitive status. To accurately assess knowledge proficiency, various pedagogical theories have emerged. Bloom’s cognitive theory, proposed in 1956 as one of the classic theories, follows the cognitive progression from foundational to advanced levels, categorizing cognition into multiple tiers including “knowing”, “understanding”, and “application”, thereby constructing a hierarchical cognitive structure. This theory is predominantly employed to frame the design of teaching objectives and guide the implementation of teaching activities. Additionally, due to the large number of students in real-world online education systems, the time required to calculate knowledge proficiency is significantly high and unacceptable. To ensure the applicability of this method in large-scale systems, there is a substantial demand for the design of a parallel prediction model to assess knowledge proficiency. The research in this paper is grounded in Bloom’s Cognitive theory, and a Bloom Cognitive Diagnosis Parallel Model (BloomCDM) for calculating knowledge proficiency is designed based on this theory. The model is founded on the concept of matrix decomposition. In the theoretical modeling phase, hierarchical and inter-hierarchical assumptions are initially established, leading to the abstraction of the mathematical model. Subsequently, subject features are mapped onto the three-tier cognitive space of “knowing”, “understanding”, and “applying” to derive the posterior distribution of the target parameters. Upon determining the objective function of the model, both student and topic characteristic parameters are computed to ascertain students’ knowledge proficiency. During the modeling process, in order to formalize the mathematical expressions of “understanding” and “application”, the notions of “knowledge group” and “higher-order knowledge group” are introduced, along with a parallel method for identifying the structure of higher-order knowledge groups. Finally, the experiments in this paper validate that the model can accurately diagnose students’ knowledge proficiency, affirming the scientific and meaningful integration of Bloom’s cognitive hierarchy in knowledge proficiency assessment
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery
Building 3D reconstruction from remote sensing images has a wide range of
applications in smart cities, photogrammetry and other fields. Methods for
automatic 3D urban building modeling typically employ multi-view images as
input to algorithms to recover point clouds and 3D models of buildings.
However, such models rely heavily on multi-view images of buildings, which are
time-intensive and limit the applicability and practicality of the models. To
solve these issues, we focus on designing an efficient DSM estimation-driven
reconstruction framework (Building3D), which aims to reconstruct 3D building
models from the input single-view remote sensing image. First, we propose a
Semantic Flow Field-guided DSM Estimation (SFFDE) network, which utilizes the
proposed concept of elevation semantic flow to achieve the registration of
local and global features. Specifically, in order to make the network semantics
globally aware, we propose an Elevation Semantic Globalization (ESG) module to
realize the semantic globalization of instances. Further, in order to alleviate
the semantic span of global features and original local features, we propose a
Local-to-Global Elevation Semantic Registration (L2G-ESR) module based on
elevation semantic flow. Our Building3D is rooted in the SFFDE network for
building elevation prediction, synchronized with a building extraction network
for building masks, and then sequentially performs point cloud reconstruction,
surface reconstruction (or CityGML model reconstruction). On this basis, our
Building3D can optionally generate CityGML models or surface mesh models of the
buildings. Extensive experiments on ISPRS Vaihingen and DFC2019 datasets on the
DSM estimation task show that our SFFDE significantly improves upon
state-of-the-arts. Furthermore, our Building3D achieves impressive results in
the 3D point cloud and 3D model reconstruction process
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