Search Results for author:
Found 8 papers, 3 papers with code
Gaussian Scenes: Pose-Free Sparse-View Scene Reconstruction using Depth-Enhanced Diffusion Priors
We also propose a novel confidence measure for Gaussian representations to allow for better detection of these artifacts.
iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning
We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets and show that our approach outperforms the baselines for classification by $2-6\%$ in the in-domain and by $6-50\%$ in the OOD setting.
A Bayesian Approach to OOD Robustness in Image Classification
This enables us to learn a transitional dictionary of vMF kernels that are intermediate between the source and target domains and train the generative model on this dictionary using the annotations on the source domain, followed by iterative refinement.
Ranked #1 on
Unsupervised Domain Adaptation
on OOD-CV
(Accuracy (Top-1) metric)
Source-Free and Image-Only Unsupervised Domain Adaptation for Category Level Object Pose Estimation
We focus on individual locally robust mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain even when the global pose is not correct.
Ranked #1 on
Unsupervised Domain Adaptation
on OOD-CV
Learning Part Segmentation from Synthetic Animals
We then benchmark Syn-to-Real animal part segmentation from SAP to PartImageNet, namely SynRealPart, with existing semantic segmentation domain adaptation methods and further improve them as our second contribution.
Animal3D: A Comprehensive Dataset of 3D Animal Pose and Shape
Animal3D consists of 3379 images collected from 40 mammal species, high-quality annotations of 26 keypoints, and importantly the pose and shape parameters of the SMAL model.
Ranked #1 on
Animal Pose Estimation
on Animal3D
Understanding Catastrophic Forgetting and Remembering in Continual Learning with Optimal Relevance Mapping
Additionally, current approaches that deal with forgetting ignore the problem of catastrophic remembering, i. e. the worsening ability to discriminate between data from different tasks.
Ranked #1 on
Continual Learning
on ImageNet-50 (5 tasks)
Adaptive Neural Connections for Sparsity Learning
We propose Adaptive Neural Connections (ANC), a method for explicitly parameterizing fine-grained neuron-to-neuron connections via adjacency matrices at each layer that are learned through backpropagation.
Ranked #1 on
Sparse Learning
on ImageNet32
