35 research outputs found
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
The vision-based perception for autonomous driving has undergone a
transformation from the bird-eye-view (BEV) representations to the 3D semantic
occupancy. Compared with the BEV planes, the 3D semantic occupancy further
provides structural information along the vertical direction. This paper
presents OccFormer, a dual-path transformer network to effectively process the
3D volume for semantic occupancy prediction. OccFormer achieves a long-range,
dynamic, and efficient encoding of the camera-generated 3D voxel features. It
is obtained by decomposing the heavy 3D processing into the local and global
transformer pathways along the horizontal plane. For the occupancy decoder, we
adapt the vanilla Mask2Former for 3D semantic occupancy by proposing
preserve-pooling and class-guided sampling, which notably mitigate the sparsity
and class imbalance. Experimental results demonstrate that OccFormer
significantly outperforms existing methods for semantic scene completion on
SemanticKITTI dataset and for LiDAR semantic segmentation on nuScenes dataset.
Code is available at \url{https://github.com/zhangyp15/OccFormer}.Comment: Code is available at https://github.com/zhangyp15/OccForme
Conditional Random Fields as Recurrent Neural Networks
Pixel-level labelling tasks, such as semantic segmentation, play a central
role in image understanding. Recent approaches have attempted to harness the
capabilities of deep learning techniques for image recognition to tackle
pixel-level labelling tasks. One central issue in this methodology is the
limited capacity of deep learning techniques to delineate visual objects. To
solve this problem, we introduce a new form of convolutional neural network
that combines the strengths of Convolutional Neural Networks (CNNs) and
Conditional Random Fields (CRFs)-based probabilistic graphical modelling. To
this end, we formulate mean-field approximate inference for the Conditional
Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.
This network, called CRF-RNN, is then plugged in as a part of a CNN to obtain a
deep network that has desirable properties of both CNNs and CRFs. Importantly,
our system fully integrates CRF modelling with CNNs, making it possible to
train the whole deep network end-to-end with the usual back-propagation
algorithm, avoiding offline post-processing methods for object delineation. We
apply the proposed method to the problem of semantic image segmentation,
obtaining top results on the challenging Pascal VOC 2012 segmentation
benchmark.Comment: This paper is published in IEEE ICCV 201
Detecting As Labeling: Rethinking LiDAR-camera Fusion in 3D Object Detection
3D object Detection with LiDAR-camera encounters overfitting in algorithm
development which is derived from the violation of some fundamental rules. We
refer to the data annotation in dataset construction for theory complementing
and argue that the regression task prediction should not involve the feature
from the camera branch. By following the cutting-edge perspective of 'Detecting
As Labeling', we propose a novel paradigm dubbed DAL. With the most classical
elementary algorithms, a simple predicting pipeline is constructed by imitating
the data annotation process. Then we train it in the simplest way to minimize
its dependency and strengthen its portability. Though simple in construction
and training, the proposed DAL paradigm not only substantially pushes the
performance boundary but also provides a superior trade-off between speed and
accuracy among all existing methods. With comprehensive superiority, DAL is an
ideal baseline for both future work development and practical deployment. The
code has been released to facilitate future work on
https://github.com/HuangJunJie2017/BEVDet
High-throughput cell-based screening reveals a role for ZNF131 as a repressor of ERalpha signaling
<p>Abstract</p> <p>Background</p> <p>Estrogen receptor α (ERα) is a transcription factor whose activity is affected by multiple regulatory cofactors. In an effort to identify the human genes involved in the regulation of ERα, we constructed a high-throughput, cell-based, functional screening platform by linking a response element (ERE) with a reporter gene. This allowed the cellular activity of ERα, in cells cotransfected with the candidate gene, to be quantified in the presence or absence of its cognate ligand E2.</p> <p>Results</p> <p>From a library of 570 human cDNA clones, we identified zinc finger protein 131 (ZNF131) as a repressor of ERα mediated transactivation. ZNF131 is a typical member of the BTB/POZ family of transcription factors, and shows both ubiquitous expression and a high degree of sequence conservation. The luciferase reporter gene assay revealed that ZNF131 inhibits ligand-dependent transactivation by ERα in a dose-dependent manner. Electrophoretic mobility shift assay clearly demonstrated that the interaction between ZNF131 and ERα interrupts or prevents ERα binding to the estrogen response element (ERE). In addition, ZNF131 was able to suppress the expression of pS2, an ERα target gene.</p> <p>Conclusion</p> <p>We suggest that the functional screening platform we constructed can be applied for high-throughput genomic screening candidate ERα-related genes. This in turn may provide new insights into the underlying molecular mechanisms of ERα regulation in mammalian cells.</p