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Found 10 papers, 2 papers with code
2023 Low-Power Computer Vision Challenge (LPCVC) Summary
This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC).
Confronting Reward Overoptimization for Diffusion Models: A Perspective of Inductive and Primacy Biases
Then, we surprisingly discover that dormant neurons in our critic model act as a regularization against overoptimization, while active neurons reflect primacy bias in this setting.
Learning Robust Generalizable Radiance Field with Visibility and Feature Augmented Point Representation
Experiments show that our model can deliver better geometries, view consistencies, and rendering quality than all counterparts and benchmarks on three datasets in both generalization and finetuning settings, preliminarily proving the potential of the new paradigm for generalizable NeRF.
GRACE: Loss-Resilient Real-Time Video through Neural Codecs
In real-time video communication, retransmitting lost packets over high-latency networks is not viable due to strict latency requirements.
Methodology for Capacity Credit Evaluation of Physical and Virtual Energy Storage in Decarbonized Power System
Energy storage (ES) and virtual energy storage (VES) are key components to realizing power system decarbonization.
RankMatch: Fostering Confidence and Consistency in Learning with Noisy Labels
Confidence-wise, we propose a novel sample selection strategy based on confidence representation voting instead of the widely-used small-loss criterion.
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.
Ranked #4 on
Source-Free Domain Adaptation
on VisDA-2017
Dite-HRNet: Dynamic Lightweight High-Resolution Network for Human Pose Estimation
A high-resolution network exhibits remarkable capability in extracting multi-scale features for human pose estimation, but fails to capture long-range interactions between joints and has high computational complexity.
Ranked #30 on
Pose Estimation
on COCO test-dev
BagPipe: Accelerating Deep Recommendation Model Training
Based on these insights, we develop Bagpipe, a system for training deep recommendation models that uses caching and prefetching to overlap remote embedding accesses with the computation.
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots
To create a common ground between the human and the learning robot, in this paper, we propose an Augmented Reality (AR) system that reveals the hidden state of the learning to the human users.
