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Found 8 papers, 1 papers with code
Event-based Video Frame Interpolation with Edge Guided Motion Refinement
Moreover, given that event data can provide accurate visual references at scene edges between consecutive frames, we introduce a learned visibility map derived from event data to adaptively mitigate the occlusion problem in the warping refinement process.
Data-efficient Event Camera Pre-training via Disentangled Masked Modeling
Our pre-training overcomes the limitations of previous methods, which either sacrifice temporal information by converting event sequences into 2D images for utilizing pre-trained image models or directly employ paired image data for knowledge distillation to enhance the learning of event streams.
Interpretation on Multi-modal Visual Fusion
In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community.
Event Voxel Set Transformer for Spatiotemporal Representation Learning on Event Streams
To fit the sparse nature of events and sufficiently explore the relationship between them, we develop a novel attention-aware model named Event Voxel Set Transformer (EVSTr) for spatiotemporal representation learning on event streams.
A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition
Recent advances in event-based research prioritize sparsity and temporal precision.
TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers
To leverage the observed findings, we propose a novel critical minority relationship-aware method based on the Transformer architecture in which the facial part relationships can be learned.
TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification
To this end, two novel modules are proposed to leverage the characteristics of bird images, namely, the hierarchy stage feature aggregation (HSFA) module and the feature in feature abstraction (FFA) module.
Ranked #8 on
Fine-Grained Image Classification
on NABirds
A Voxel Graph CNN for Object Classification with Event Cameras
This study aims to address the core problem of balancing accuracy and model complexity for event-based classification models.
