12 Result(s)
within
Chapter
Abhinav Shrivastava
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Chapter and Conference Paper
Neural Space-Filling Curves
We present Neural Space-filling Curves (SFCs), a data-driven approach to infer a context-based scan order for a set of images. Linear ordering of pixels forms the basis for many applications such as video scrambl...
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Chapter and Conference Paper
Burn After Reading: Online Adaptation for Cross-domain Streaming Data
In the context of online privacy, many methods propose complex security preserving measures to protect sensitive data. In this paper we note that: not storing any sensitive data is the best form of security. W...
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Chapter and Conference Paper
Learning Semantic Correspondence with Sparse Annotations
Finding dense semantic correspondence is a fundamental problem in computer vision, which remains challenging in complex scenes due to background clutter, extreme intra-class variation, and a severe lack of gro...
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Chapter and Conference Paper
Improving Closed and Open-Vocabulary Attribute Prediction Using Transformers
We study recognizing attributes for objects in visual scenes. We consider attributes to be any phrases that describe an object’s physical and semantic properties, and its relationships with other objects. Exis...
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Chapter and Conference Paper
Curriculum Manager for Source Selection in Multi-source Domain Adaptation
The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. In this paper, we proposed an adversarial agent that le...
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Chapter and Conference Paper
Quantization Guided JPEG Artifact Correction
The JPEG image compression algorithm is the most popular method of image compression because of it’s ability for large compression ratios. However, to achieve such high compression, information is lost. For ag...
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Chapter and Conference Paper
A Generic Visualization Approach for Convolutional Neural Networks
Retrieval networks are essential for searching and indexing. Compared to classification networks, attention visualization for retrieval networks is hardly studied. We formulate attention visualization as a con...
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Chapter and Conference Paper
Tracking Emerges by Colorizing Videos
We use large amounts of unlabeled video to learn models for visual tracking without manual human supervision. We leverage the natural temporal coherency of color to create a model that learns to colorize gray-...
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Chapter and Conference Paper
Actor-Centric Relation Network
Current state-of-the-art approaches for spatio-temporal action localization rely on detections at the frame level and model temporal context with 3D ConvNets. Here, we go one step further and model spatio-temp...
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Chapter and Conference Paper
Contextual Priming and Feedback for Faster R-CNN
The field of object detection has seen dramatic performance improvements in the last few years. Most of these gains are attributed to bottom-up, feedforward ConvNet frameworks. However, in case of humans, top-...
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Chapter and Conference Paper
Constrained Semi-Supervised Learning Using Attributes and Comparative Attributes
We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-c...
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Chapter
Bacterial Asparaginase: A Potential Antineoplastic Agent for Treatment of Acute Lymphoblastic Leukemia
Among the pediatric cancer in developed countries, acute leukemia constitutes major part with affecting 30–45 per 1,000,000 children each year. Although one thirds of acute lymphoblastic leukemia cases are cu...
