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Chapter and Conference Paper
Waymo Open Dataset: Panoramic Video Panoptic Segmentation
Panoptic image segmentation is the computer vision task of finding groups of pixels in an image and assigning semantic classes and object instance identifiers to them. Research in image segmentation has become...
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Article
View-Invariant, Occlusion-Robust Probabilistic Embedding for Human Pose
Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant ...
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Chapter and Conference Paper
k-means Mask Transformer
The rise of transformers in vision tasks not only advances network backbone designs, but also starts a brand-new page to achieve end-to-end image recognition (e.g., object detection and panoptic segmentation). Or...
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Article
Open AccessA novel dual HDAC and HSP90 inhibitor, MPT0G449, downregulates oncogenic pathways in human acute leukemia in vitro and in vivo
Acute leukemia is a highly heterogeneous disease; therefore, combination therapy is commonly used for patient treatment. Drug–drug interaction is a major concern of combined therapy; hence, dual/multi-target i...
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Chapter and Conference Paper
Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation
Convolution exploits locality for efficiency at a cost of missing long range context. Self-attention has been adopted to augment CNNs with non-local interactions. Recent works prove it possible to stack self-a...
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Chapter and Conference Paper
Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art resul...
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Chapter and Conference Paper
View-Invariant Probabilistic Embedding for Human Pose
Depictions of similar human body configurations can vary with changing viewpoints. Using only 2D information, we would like to enable vision algorithms to recognize similarity in human body poses across multip...
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Article
The Devil is in the Decoder: Classification, Regression and GANs
Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image. Models for such problems usually consist of encoders which decrease...
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Chapter and Conference Paper
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles b...
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Chapter and Conference Paper
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing ...
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Chapter and Conference Paper
Zoom Better to See Clearer: Human and Object Parsing with Hierarchical Auto-Zoom Net
Parsing articulated objects, e.g. humans and animals, into semantic parts (e.g. head, body and arms, etc.) from natural images is a challenging and fundamental problem in computer vision. A big difficulty is the ...
