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Found 6 papers, 1 papers with code
NiSNN-A: Non-iterative Spiking Neural Networks with Attention with Application to Motor Imagery EEG Classification
Motor imagery, an important category in electroencephalogram (EEG) research, often intersects with scenarios demanding low energy consumption, such as portable medical devices and isolated environment operations.
Helping Hands: An Object-Aware Ego-Centric Video Recognition Model
We demonstrate the performance of the object-aware representations learnt by our model, by: (i) evaluating it for strong transfer, i. e. through zero-shot testing, on a number of downstream video-text retrieval and classification benchmarks; and (ii) by using the representations learned as input for long-term video understanding tasks (e. g. Episodic Memory in Ego4D).
Making the Most of What You Have: Adapting Pre-trained Visual Language Models in the Low-data Regime
Large-scale visual language models are widely used as pre-trained models and then adapted for various downstream tasks.
Is an Object-Centric Video Representation Beneficial for Transfer?
The model learns a set of object-centric summary vectors for the video, and uses these vectors to fuse the visual and spatio-temporal trajectory 'modalities' of the video clip.
Temporal Query Networks for Fine-grained Video Understanding
It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query.
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Action Recognition
on Diving-48
Adaptive Text Recognition through Visual Matching
In this work, our objective is to address the problems of generalization and flexibility for text recognition in documents.
