41 research outputs found
Learning Compositional Visual Concepts with Mutual Consistency
Compositionality of semantic concepts in image synthesis and analysis is
appealing as it can help in decomposing known and generatively recomposing
unknown data. For instance, we may learn concepts of changing illumination,
geometry or albedo of a scene, and try to recombine them to generate physically
meaningful, but unseen data for training and testing. In practice however we
often do not have samples from the joint concept space available: We may have
data on illumination change in one data set and on geometric change in another
one without complete overlap. We pose the following question: How can we learn
two or more concepts jointly from different data sets with mutual consistency
where we do not have samples from the full joint space? We present a novel
answer in this paper based on cyclic consistency over multiple concepts,
represented individually by generative adversarial networks (GANs). Our method,
ConceptGAN, can be understood as a drop in for data augmentation to improve
resilience for real world applications. Qualitative and quantitative
evaluations demonstrate its efficacy in generating semantically meaningful
images, as well as one shot face verification as an example application.Comment: 10 pages, 8 figures, 4 tables, CVPR 201
Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection
The primary bottleneck towards obtaining good recognition performance in IR
images is the lack of sufficient labeled training data, owing to the cost of
acquiring such data. Realizing that object detection methods for the RGB
modality are quite robust (at least for some commonplace classes, like person,
car, etc.), thanks to the giant training sets that exist, in this work we seek
to leverage cues from the RGB modality to scale object detectors to the IR
modality, while preserving model performance in the RGB modality. At the core
of our method, is a novel tensor decomposition method called TensorFact which
splits the convolution kernels of a layer of a Convolutional Neural Network
(CNN) into low-rank factor matrices, with fewer parameters than the original
CNN. We first pretrain these factor matrices on the RGB modality, for which
plenty of training data are assumed to exist and then augment only a few
trainable parameters for training on the IR modality to avoid over-fitting,
while encouraging them to capture complementary cues from those trained only on
the RGB modality. We validate our approach empirically by first assessing how
well our TensorFact decomposed network performs at the task of detecting
objects in RGB images vis-a-vis the original network and then look at how well
it adapts to IR images of the FLIR ADAS v1 dataset. For the latter, we train
models under scenarios that pose challenges stemming from data paucity. From
the experiments, we observe that: (i) TensorFact shows performance gains on RGB
images; (ii) further, this pre-trained model, when fine-tuned, outperforms a
standard state-of-the-art object detector on the FLIR ADAS v1 dataset by about
4% in terms of mAP 50 score.Comment: Accepted to ICCV 2023, LIMIT Workshop. The first two authors
contributed equall
Are Deep Neural Networks SMARTer than Second Graders?
Recent times have witnessed an increasing number of applications of deep
neural networks towards solving tasks that require superior cognitive
abilities, e.g., playing Go, generating art, ChatGPT, etc. Such a dramatic
progress raises the question: how generalizable are neural networks in solving
problems that demand broad skills? To answer this question, we propose SMART: a
Simple Multimodal Algorithmic Reasoning Task and the associated SMART-101
dataset, for evaluating the abstraction, deduction, and generalization
abilities of neural networks in solving visuo-linguistic puzzles designed
specifically for children in the 6--8 age group. Our dataset consists of 101
unique puzzles; each puzzle comprises a picture and a question, and their
solution needs a mix of several elementary skills, including arithmetic,
algebra, and spatial reasoning, among others. To scale our dataset towards
training deep neural networks, we programmatically generate entirely new
instances for each puzzle, while retaining their solution algorithm. To
benchmark performances on SMART-101, we propose a vision and language
meta-learning model using varied state-of-the-art backbones. Our experiments
reveal that while powerful deep models offer reasonable performances on puzzles
in a supervised setting, they are not better than random accuracy when analyzed
for generalization. We also evaluate the recent ChatGPT and other large
language models on a part of SMART-101 and find that while these models show
convincing reasoning abilities, the answers are often incorrect.Comment: Accepted at CVPR 2023. For the SMART-101 dataset, see
https://doi.org/10.5281/zenodo.776179
Enhanced Differentiation of Three-Gene-Reprogrammed Induced Pluripotent Stem Cells into Adipocytes via Adenoviral-Mediated PGC-1α Overexpression
Induced pluripotent stem cells formed by the introduction of only three factors, Oct4/Sox2/Klf4 (3-gene iPSCs), may provide a safer option for stem cell-based therapy than iPSCs conventionally introduced with four-gene iPSCs. Peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α) plays an important role during brown fat development. However, the potential roles of PGC-1α in regulating mitochondrial biogenesis and the differentiation of iPSCs are still unclear. Here, we investigated the effects of adenovirus-mediated PGC-1α overexpression in 3-gene iPSCs. PGC-1α overexpression resulted in increased mitochondrial mass, reactive oxygen species production, and oxygen consumption. Microarray-based bioinformatics showed that the gene expression pattern of PGC-1α-overexpressing 3-gene iPSCs resembled the expression pattern observed in adipocytes. Furthermore, PGC-1α overexpression enhanced adipogenic differentiation and the expression of several brown fat markers, including uncoupling protein-1, cytochrome C, and nuclear respiratory factor-1, whereas it inhibited the expression of the white fat marker uncoupling protein-2. Furthermore, PGC-1α overexpression significantly suppressed osteogenic differentiation. These data demonstrate that PGC-1α directs the differentiation of 3-gene iPSCs into adipocyte-like cells with features of brown fat cells. This may provide a therapeutic strategy for the treatment of mitochondrial disorders and obesity
Women with endometriosis have higher comorbidities: Analysis of domestic data in Taiwan
AbstractEndometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities