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Found 14 papers, 5 papers with code
Distinguishing between Dementia with Lewy bodies (DLB) and Alzheimer’s Disease (AD) using Mental Health Records: a Classification Approach
To the best of our knowledge, this is the first attempt to distinguish DLB from AD using mental health records, and to improve the reliability of DLB predictions.
Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
While various models and computational tools have been proposed for structure and property analysis of molecules, generating molecules that conform to all desired structures and properties remains a challenge.
Edge Caching Based on Deep Reinforcement Learning and Transfer Learning
This paper addresses the escalating challenge of redundant data transmission in networks.
nuScenes Knowledge Graph -- A comprehensive semantic representation of traffic scenes for trajectory prediction
Further, we present nuScenes Knowledge Graph (nSKG), a knowledge graph for the nuScenes dataset, that models explicitly all scene participants and road elements, as well as their semantic and spatial relationships.
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources.
Scene Text Recognition with Semantics
Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise.
Contrastive Video-Language Learning with Fine-grained Frame Sampling
However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Guiding Visual Question Generation
In traditional Visual Question Generation (VQG), most images have multiple concepts (e. g. objects and categories) for which a question could be generated, but models are trained to mimic an arbitrary choice of concept as given in their training data.
Cross-Modal Generative Augmentation for Visual Question Answering
Experiments on Visual Question Answering as downstream task demonstrate the effectiveness of the proposed generative model, which is able to improve strong UpDn-based models to achieve state-of-the-art performance.
Exploring Supervised and Unsupervised Rewards in Machine Translation
Reinforcement Learning (RL) is a powerful framework to address the discrepancy between loss functions used during training and the final evaluation metrics to be used at test time.
Latent Variable Models for Visual Question Answering
Current work on Visual Question Answering (VQA) explore deterministic approaches conditioned on various types of image and question features.
Actions as Moving Points
The existing action tubelet detectors often depend on heuristic anchor design and placement, which might be computationally expensive and sub-optimal for precise localization.
Ranked #5 on
Action Detection
on UCF101-24
Is artificial data useful for biomedical Natural Language Processing algorithms?
A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility.
