44 research outputs found
DETEKSI PENYAKIT TUBERKULOSIS MELALUI SEGMENTASI CITRA MENGGUNAKAN ALGORITMA K-MEANS
Tuberkulosis telah menjadi penyakit yang sangat berbahaya, penularan yang cepat dan mudah menjadi penyakit menular paling berbahaya di dunia saat ini. Deteksi bakteri mycobacterium tuberkulosis pun diperlukan untuk mempercepat diagnosis pasien, agar pasien dapat segera diobati dan penularan dapat dihentikan. Dalam penelitian ini, sebuah pendekatan segmentasi citra yang menggabungkan model warna LAB dan algoritma clustering K-Means diajukan untuk memisahkan dengan akurat area yang berisi bakteri tuberculosis dalam citra dari latar belakang. Pertama-tama, citra mikroskopis diubah ke dalam ruang warna LAB guna mengekstraksi komponen warna yang paling sensitif terhadap perbedaan intensitas dalam citra bakteri mycobacterium tuberculosis. Selanjutnya, melalui penerapan algoritma K-Means clustering, piksel-piksel citra dikelompokkan menjadi beberapa kelompok berdasarkan perbedaan intensitasnya. Hasil eksperimen menunjukkan bahwa pendekatan ini mampu mengisolasi area yang berisi bakteri mycobacterium tuberculosis dalam citra mikroskopis dengan akurasi dan efisiensi yang tinggi. Meskipun hasil akurasi yang tinggi didapatkan dengan cara diamati secara visual, penting untuk dicatat bahwa validasi akurasi segmentasi ini menjadi tantangan karena kurangnya cara yang objektif untuk memvalidasi keberadaan bakteri tuberkulosis dalam citra hasil. Namun, hasil penelitian ini memberikan indikasi yang kuat bahwa pendekatan segmentasi yang diusulkan ini memiliki potensi sebagai langkah awal dalam pengembangan sistem deteksi otomatis bakteri tuberculosis yang lebih canggih
Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation
Cross-resolution face recognition has become a challenging problem for modern
deep face recognition systems. It aims at matching a low-resolution probe image
with high-resolution gallery images registered in a database. Existing methods
mainly leverage prior information from high-resolution images by either
reconstructing facial details with super-resolution techniques or learning a
unified feature space. To address this challenge, this paper proposes a new
approach that enforces the network to focus on the discriminative information
stored in the low-frequency components of a low-resolution image. A
cross-resolution knowledge distillation paradigm is first employed as the
learning framework. Then, an identity-preserving network, WaveResNet, and a
wavelet similarity loss are designed to capture low-frequency details and boost
performance. Finally, an image degradation model is conceived to simulate more
realistic low-resolution training data. Consequently, extensive experimental
results show that the proposed method consistently outperforms the baseline
model and other state-of-the-art methods across a variety of image resolutions
Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images
Many anomaly detection approaches, especially deep learning methods, have
been recently developed to identify abnormal image morphology by only employing
normal images during training. Unfortunately, many prior anomaly detection
methods were optimized for a specific "known" abnormality (e.g., brain tumor,
bone fraction, cell types). Moreover, even though only the normal images were
used in the training process, the abnormal images were often employed during
the validation process (e.g., epoch selection, hyper-parameter tuning), which
might leak the supposed ``unknown" abnormality unintentionally. In this study,
we investigated these two essential aspects regarding universal anomaly
detection in medical images by (1) comparing various anomaly detection methods
across four medical datasets, (2) investigating the inevitable but often
neglected issues on how to unbiasedly select the optimal anomaly detection
model during the validation phase using only normal images, and (3) proposing a
simple decision-level ensemble method to leverage the advantage of different
kinds of anomaly detection without knowing the abnormality. The results of our
experiments indicate that none of the evaluated methods consistently achieved
the best performance across all datasets. Our proposed method enhanced the
robustness of performance in general (average AUC 0.956)
Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling
Training high-quality instance segmentation models requires an abundance of
labeled images with instance masks and classifications, which is often
expensive to procure. Active learning addresses this challenge by striving for
optimum performance with minimal labeling cost by selecting the most
informative and representative images for labeling. Despite its potential,
active learning has been less explored in instance segmentation compared to
other tasks like image classification, which require less labeling. In this
study, we propose a post-hoc active learning algorithm that integrates
uncertainty-based sampling with diversity-based sampling. Our proposed
algorithm is not only simple and easy to implement, but it also delivers
superior performance on various datasets. Its practical application is
demonstrated on a real-world overhead imagery dataset, where it increases the
labeling efficiency fivefold.Comment: UNCV ICCV 202
PRSNet: A Masked Self-Supervised Learning Pedestrian Re-Identification Method
In recent years, self-supervised learning has attracted widespread academic
debate and addressed many of the key issues of computer vision. The present
research focus is on how to construct a good agent task that allows for
improved network learning of advanced semantic information on images so that
model reasoning is accelerated during pre-training of the current task. In
order to solve the problem that existing feature extraction networks are
pre-trained on the ImageNet dataset and cannot extract the fine-grained
information in pedestrian images well, and the existing pre-task of contrast
self-supervised learning may destroy the original properties of pedestrian
images, this paper designs a pre-task of mask reconstruction to obtain a
pre-training model with strong robustness and uses it for the pedestrian
re-identification task. The training optimization of the network is performed
by improving the triplet loss based on the centroid, and the mask image is
added as an additional sample to the loss calculation, so that the network can
better cope with the pedestrian matching in practical applications after the
training is completed. This method achieves about 5% higher mAP on Marker1501
and CUHK03 data than existing self-supervised learning pedestrian
re-identification methods, and about 1% higher for Rank1, and ablation
experiments are conducted to demonstrate the feasibility of this method. Our
model code is located at https://github.com/ZJieX/prsnet
Long Story Short: a Summarize-then-Search Method for Long Video Question Answering
Large language models such as GPT-3 have demonstrated an impressive
capability to adapt to new tasks without requiring task-specific training data.
This capability has been particularly effective in settings such as narrative
question answering, where the diversity of tasks is immense, but the available
supervision data is small. In this work, we investigate if such language models
can extend their zero-shot reasoning abilities to long multimodal narratives in
multimedia content such as drama, movies, and animation, where the story plays
an essential role. We propose Long Story Short, a framework for narrative video
QA that first summarizes the narrative of the video to a short plot and then
searches parts of the video relevant to the question. We also propose to
enhance visual matching with CLIPCheck. Our model outperforms state-of-the-art
supervised models by a large margin, highlighting the potential of zero-shot QA
for long videos.Comment: Published in BMVC 202
SimSwap: An Efficient Framework For High Fidelity Face Swapping
We propose an efficient framework, called Simple Swap (SimSwap), aiming for
generalized and high fidelity face swapping. In contrast to previous approaches
that either lack the ability to generalize to arbitrary identity or fail to
preserve attributes like facial expression and gaze direction, our framework is
capable of transferring the identity of an arbitrary source face into an
arbitrary target face while preserving the attributes of the target face. We
overcome the above defects in the following two ways. First, we present the ID
Injection Module (IIM) which transfers the identity information of the source
face into the target face at feature level. By using this module, we extend the
architecture of an identity-specific face swapping algorithm to a framework for
arbitrary face swapping. Second, we propose the Weak Feature Matching Loss
which efficiently helps our framework to preserve the facial attributes in an
implicit way. Extensive experiments on wild faces demonstrate that our SimSwap
is able to achieve competitive identity performance while preserving attributes
better than previous state-of-the-art methods. The code is already available on
github: https://github.com/neuralchen/SimSwap.Comment: Accepted by ACMMM 202
CCFace: Classification Consistency for Low-Resolution Face Recognition
In recent years, deep face recognition methods have demonstrated impressive
results on in-the-wild datasets. However, these methods have shown a
significant decline in performance when applied to real-world low-resolution
benchmarks like TinyFace or SCFace. To address this challenge, we propose a
novel classification consistency knowledge distillation approach that transfers
the learned classifier from a high-resolution model to a low-resolution
network. This approach helps in finding discriminative representations for
low-resolution instances. To further improve the performance, we designed a
knowledge distillation loss using the adaptive angular penalty inspired by the
success of the popular angular margin loss function. The adaptive penalty
reduces overfitting on low-resolution samples and alleviates the convergence
issue of the model integrated with data augmentation. Additionally, we utilize
an asymmetric cross-resolution learning approach based on the state-of-the-art
semi-supervised representation learning paradigm to improve discriminability on
low-resolution instances and prevent them from forming a cluster. Our proposed
method outperforms state-of-the-art approaches on low-resolution benchmarks,
with a three percent improvement on TinyFace while maintaining performance on
high-resolution benchmarks.Comment: 2023 IEEE International Joint Conference on Biometrics (IJCB