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Found 5 papers, 3 papers with code
Perceiving Longer Sequences With Bi-Directional Cross-Attention Transformers
We present a novel bi-directional Transformer architecture (BiXT) which scales linearly with input size in terms of computational cost and memory consumption, but does not suffer the drop in performance or limitation to only one input modality seen with other efficient Transformer-based approaches.
Rethinking Generalization in Few-Shot Classification
Single image-level annotations only correctly describe an often small subset of an image's content, particularly when complex real-world scenes are depicted.
On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation
Inspired by the concept of preconditioning, we propose a novel method to increase adaptation speed for gradient-based meta-learning methods without incurring extra parameters.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.
Learning Topometric Semantic Maps from Occupancy Grids
Today's mobile robots are expected to operate in complex environments they share with humans.
