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Found 10 papers, 1 papers with code
HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction
Concretely, we introduce a hybrid representation called HIQuery to represent all map elements, and propose a point-element interactor to interactively extract and encode the hybrid information of elements, e. g. point position and element shape, into the HIQuery.
BackTrack: Robust template update via Backward Tracking of candidate template
An effective method to tackle these challenges is template update, which updates the template to reflect the change of appearance in the target object during tracking.
Object-Centric Multi-Task Learning for Human Instances
Human is one of the most essential classes in visual recognition tasks such as detection, segmentation, and pose estimation.
Slot-VPS: Object-centric Representation Learning for Video Panoptic Segmentation
We encode all panoptic entities in a video, including both foreground instances and background semantics, with a unified representation called panoptic slots.
Controllable Image Restoration for Under-Display Camera in Smartphones
However, this causes inevitable image degradation in the form of spatially variant blur and noise because of the opaque display in front of the camera.
RaScaNet: Learning Tiny Models by Raster-Scanning Images
RaScaNet reads only a few rows of pixels at a time using a convolutional neural network and then sequentially learns the representation of the whole image using a recurrent neural network.
Large Scale Multi-Illuminant (LSMI) Dataset for Developing White Balance Algorithm Under Mixed Illumination
Images in our dataset are mostly captured with illuminants existing in the scene, and the ground truth illumination is computed by taking the difference between the images with different illumination combination.
Joint Learning of Generative Translator and Classifier for Visually Similar Classes
In this paper, we propose a Generative Translation Classification Network (GTCN) for improving visual classification accuracy in settings where classes are visually similar and data is scarce.
Deep generative-contrastive networks for facial expression recognition
In this paper, we propose to utilize contrastive representation that embeds a distinctive expressive factor for a discriminative purpose.
Facial Expression Recognition
Facial Expression Recognition (FER)
Rotating Your Face Using Multi-Task Deep Neural Network
This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity.
