Search Results for author:
Found 15 papers, 10 papers with code
WorDepth: Variational Language Prior for Monocular Depth Estimation
To test this, we focus on monocular depth estimation, the problem of predicting a dense depth map from a single image, but with an additional text caption describing the scene.
Test-Time Adaptation for Depth Completion
During test time, sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i. e., adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain.
Binding Touch to Everything: Learning Unified Multimodal Tactile Representations
We introduce UniTouch, a unified tactile model for vision-based touch sensors connected to multiple modalities, including vision, language, and sound.
AugUndo: Scaling Up Augmentations for Unsupervised Depth Completion
The sparse depth modality have seen even less as intensity transformations alter the scale of the 3D scene, and geometric transformations may decimate the sparse points during resampling.
Divide-and-Conquer the NAS puzzle in Resource Constrained Federated Learning Systems
In this paper, we propose DC-NAS -- a divide-and-conquer approach that performs supernet-based Neural Architecture Search (NAS) in a federated system by systematically sampling the search space.
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
However, some essential questions exist pertaining to SNNs that are little studied: Do SNNs trained with surrogate gradient learn different representations from traditional Artificial Neural Networks (ANNs)?
Exploring Temporal Information Dynamics in Spiking Neural Networks
After training, we observe that information becomes highly concentrated in earlier few timesteps, a phenomenon we refer to as temporal information concentration.
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes.
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
To scale up a pruning technique towards deep SNNs, we investigate Lottery Ticket Hypothesis (LTH) which states that dense networks contain smaller subnetworks (i. e., winning tickets) that achieve comparable performance to the dense networks.
Addressing Client Drift in Federated Continual Learning with Adaptive Optimization
However, there is little attention towards additional challenges emerging when federated aggregation is performed in a continual learning system.
Neuromorphic Data Augmentation for Training Spiking Neural Networks
In an effort to minimize this generalization gap, we propose Neuromorphic Data Augmentation (NDA), a family of geometric augmentations specifically designed for event-based datasets with the goal of significantly stabilizing the SNN training and reducing the generalization gap between training and test performance.
Ranked #1 on
Event data classification
on CIFAR10-DVS
(using extra training data)
Rate Coding or Direct Coding: Which One is Better for Accurate, Robust, and Energy-efficient Spiking Neural Networks?
Then, we measure the robustness of the coding techniques on two adversarial attack methods.
Neural Architecture Search for Spiking Neural Networks
Interestingly, SNASNet found by our search algorithm achieves higher performance with backward connections, demonstrating the importance of designing SNN architecture for suitably using temporal information.
Robust Federated Learning with Noisy Labels
To solve these problems, we introduce a novel federated learning scheme that the server cooperates with local models to maintain consistent decision boundaries by interchanging class-wise centroids.
Meta Batch-Instance Normalization for Generalizable Person Re-Identification
To this end, we combine learnable batch-instance normalization layers with meta-learning and investigate the challenging cases caused by both batch and instance normalization layers.
