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Found 6 papers, 3 papers with code
Divide and Conquer: Rethinking the Training Paradigm of Neural Radiance Fields
Neural radiance fields (NeRFs) have exhibited potential in synthesizing high-fidelity views of 3D scenes but the standard training paradigm of NeRF presupposes an equal importance for each image in the training set.
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.
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.
Adaptive Poincaré Point to Set Distance for Few-Shot Classification
To this end, we formulate the metric as a weighted sum on the tangent bundle of the hyperbolic space and develop a mechanism to obtain the weights adaptively and based on the constellation of the points.
Learning Online for Unified Segmentation and Tracking Models
A segmentation model cannot easily learn from prior information given in the visual tracking scenario.
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.
