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Found 11 papers, 0 papers with code
Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification
We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task.
Exploring Cross-Domain Pretrained Model for Hyperspectral Image Classification
In addition, we have verified that our approach effectively reduces the overfitting issue, enabling us to deepen the model up to 13 layers (from 9) without compromising the accuracy.
Semantics to Space(S2S): Embedding semantics into spatial space for zero-shot verb-object query inferencing
Our approach is powered by Semantics-to-Space (S2S) architecture where semantics derived from the residing objects are embedded into a spatial space of the visual stream.
S-DOD-CNN: Doubly Injecting Spatially-Preserved Object Information for Event Recognition
We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way.
Is Pretraining Necessary for Hyperspectral Image Classification?
To answer the first question, we have devised an approach that pre-trains a network on multiple source datasets that differ in their hyperspectral characteristics and fine-tunes on a target dataset.
DOD-CNN: Doubly-injecting Object Information for Event Recognition
Recognizing an event in an image can be enhanced by detecting relevant objects in two ways: 1) indirectly utilizing object detection information within the unified architecture or 2) directly making use of the object detection output results.
Object and Text-guided Semantics for CNN-based Activity Recognition
We further improve upon the multitask learning approach by exploiting a text-guided semantic space to select the most relevant objects with respect to the target activities.
Cross-domain CNN for Hyperspectral Image Classification
To cope with this problem, we propose a novel cross-domain CNN containing the shared parameters which can co-learn across multiple hyperspectral datasets.
ME R-CNN: Multi-Expert R-CNN for Object Detection
To address this problem, we introduce a practical training strategy which is tailored to optimize ME, EAN, and the shared network in an end-to-end fashion.
IOD-CNN: Integrating Object Detection Networks for Event Recognition
Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network.
Exploitation of Semantic Keywords for Malicious Event Classification
Learning an event classifier is challenging when the scenes are semantically different but visually similar.
