15 research outputs found
Self-supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban Scenes
Robust autonomous driving requires agents to accurately identify unexpected
areas in urban scenes. To this end, some critical issues remain open: how to
design advisable metric to measure anomalies, and how to properly generate
training samples of anomaly data? Previous effort usually resorts to
uncertainty estimation and sample synthesis from classification tasks, which
ignore the context information and sometimes requires auxiliary datasets with
fine-grained annotations. On the contrary, in this paper, we exploit the strong
context-dependent nature of segmentation task and design an energy-guided
self-supervised frameworks for anomaly segmentation, which optimizes an anomaly
head by maximizing the likelihood of self-generated anomaly pixels. To this
end, we design two estimators for anomaly likelihood estimation, one is a
simple task-agnostic binary estimator and the other depicts anomaly likelihood
as residual of task-oriented energy model. Based on proposed estimators, we
further incorporate our framework with likelihood-guided mask refinement
process to extract informative anomaly pixels for model training. We conduct
extensive experiments on challenging Fishyscapes and Road Anomaly benchmarks,
demonstrating that without any auxiliary data or synthetic models, our method
can still achieves competitive performance to other SOTA schemes
Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
Collecting large-scale datasets is crucial for training deep models,
annotating the data, however, inevitably yields noisy labels, which poses
challenges to deep learning algorithms. Previous efforts tend to mitigate this
problem via identifying and removing noisy samples or correcting their labels
according to the statistical properties (e.g., loss values) among training
samples. In this paper, we aim to tackle this problem from a new perspective,
delving into the deep feature maps, we empirically find that models trained
with clean and mislabeled samples manifest distinguishable activation feature
distributions. From this observation, a novel robust training approach termed
adversarial noisy masking is proposed. The idea is to regularize deep features
with a label quality guided masking scheme, which adaptively modulates the
input data and label simultaneously, preventing the model to overfit noisy
samples. Further, an auxiliary task is designed to reconstruct input data, it
naturally provides noise-free self-supervised signals to reinforce the
generalization ability of deep models. The proposed method is simple and
flexible, it is tested on both synthetic and real-world noisy datasets, where
significant improvements are achieved over previous state-of-the-art methods
Rethinking Mobile Block for Efficient Attention-based Models
This paper focuses on developing modern, efficient, lightweight models for
dense predictions while trading off parameters, FLOPs, and performance.
Inverted Residual Block (IRB) serves as the infrastructure for lightweight
CNNs, but no counterpart has been recognized by attention-based studies. This
work rethinks lightweight infrastructure from efficient IRB and effective
components of Transformer from a unified perspective, extending CNN-based IRB
to attention-based models and abstracting a one-residual Meta Mobile Block
(MMB) for lightweight model design. Following simple but effective design
criterion, we deduce a modern Inverted Residual Mobile Block (iRMB) and build a
ResNet-like Efficient MOdel (EMO) with only iRMB for down-stream tasks.
Extensive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks
demonstrate the superiority of our EMO over state-of-the-art methods, e.g.,
EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass equal-order
CNN-/Attention-based models, while trading-off the parameter, efficiency, and
accuracy well: running 2.8-4.0x faster than EdgeNeXt on iPhone14
How to be prepared for the next pandemic?
Wouldn't it be great if you could have your own virus detection facility at home, or even in your pocket? That's what Boshen Liang & his colleagues at imec & Ku Leuven are working on via so-called lab-on-chip technology. In this way, we could drastically increase the capacity to test people during a possible next pandemic.status: Published onlin
Large-area compatible fabrication and optimization of micropump aided by vorticity-based algorithm
status: publishe
CHROMATIX: computing the functional landscape of many-body chromatin interactions in transcriptionally active loci from deconvolved single cells
Chromatin interactions are important for gene regulation and cellular specialization. Emerging evidence suggests many-body spatial interactions play important roles in condensing super-enhancer regions into a cohesive transcriptional apparatus. Chromosome conformation studies using Hi-C are limited to pairwise, population-averaged interactions; therefore unsuitable for direct assessment of many-body interactions. We describe a computational model, CHROMATIX, which reconstructs ensembles of single-cell chromatin structures by deconvolving Hi-C data and identifies significant many-body interactions. For a diverse set of highly active transcriptional loci with at least 2 super-enhancers, we detail the many-body functional landscape and show DNase accessibility, POLR2A binding, and decreased H3K27me3 are predictive of interaction-enriched regions
FRIH: Fine-grained Region-aware Image Harmonization
Image harmonization aims to generate a more realistic appearance of
foreground and background for a composite image. Existing methods perform the
same harmonization process for the whole foreground. However, the implanted
foreground always contains different appearance patterns. All the existing
solutions ignore the difference of each color block and losing some specific
details. Therefore, we propose a novel global-local two stages framework for
Fine-grained Region-aware Image Harmonization (FRIH), which is trained
end-to-end. In the first stage, the whole input foreground mask is used to make
a global coarse-grained harmonization. In the second stage, we adaptively
cluster the input foreground mask into several submasks by the corresponding
pixel RGB values in the composite image. Each submask and the coarsely adjusted
image are concatenated respectively and fed into a lightweight cascaded module,
adjusting the global harmonization performance according to the region-aware
local feature. Moreover, we further designed a fusion prediction module by
fusing features from all the cascaded decoder layers together to generate the
final result, which could utilize the different degrees of harmonization
results comprehensively. Without bells and whistles, our FRIH algorithm
achieves the best performance on iHarmony4 dataset (PSNR is 38.19 dB) with a
lightweight model. The parameters for our model are only 11.98 M, far below the
existing methods
Calibrated Teacher for Sparsely Annotated Object Detection
Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. As a result, the incomplete annotation in each image could provide misleading supervision and harm the training. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. Such a mechanism is sensitive to the threshold of the pseudo label score. However, the effective threshold is different in different training stages and among different object detectors. Therefore, the current methods with fixed thresholds have sub-optimal performance, and are difficult to be applied to other detectors. In order to resolve this obstacle, we propose a Calibrated Teacher, of which the confidence estimation of the prediction is well calibrated to match its real precision. In this way, different detectors in different training stages would share a similar distribution of the output confidence, so that multiple detectors could share the same fixed threshold and achieve better performance. Furthermore, we present a simple but effective Focal IoU Weight (FIoU) for the classification loss. FIoU aims at reducing the loss weight of false negative samples caused by the missing annotation, and thus works as the complement of the teacher-student paradigm. Extensive experiments show that our methods set new state-of-the-art under all different sparse settings in COCO. Code will be available at https://github.com/Whileherham/CalibratedTeacher