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Found 7 papers, 7 papers with code
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
We argue that interactive and embodied visual AI has reached a stage of development similar to visual recognition prior to the advent of these ecosystems.
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
To foster research towards the goal of general ML methods, we introduce a new unified evaluation framework - FLUID (Flexible Sequential Data).
LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes
We further quantitatively measure the quality of our codes by applying it to the efficient image retrieval as well as out-of-distribution (OOD) detection problems.
Task Adaptive Parameter Sharing for Multi-Task Learning
TAPS solves a joint optimization problem which determines which layers to share with the base model and the value of the task-specific weights.
Matryoshka Representation Learning
The flexibility within the learned Matryoshka Representations offer: (a) up to 14x smaller embedding size for ImageNet-1K classification at the same level of accuracy; (b) up to 14x real-world speed-ups for large-scale retrieval on ImageNet-1K and 4K; and (c) up to 2% accuracy improvements for long-tail few-shot classification, all while being as robust as the original representations.
Ranked #25 on
Image Classification
on ObjectNet
(using extra training data)
Neural Radiance Field Codebooks
Compositional representations of the world are a promising step towards enabling high-level scene understanding and efficient transfer to downstream tasks.
Neural Priming for Sample-Efficient Adaptation
Performing lightweight updates on the recalled data significantly improves accuracy across a variety of distribution shift and transfer learning benchmarks.
