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Found 8 papers, 8 papers with code
Generalizable Person Re-identification via Balancing Alignment and Uniformity
To address this issue, we propose a novel framework, Balancing Alignment and Uniformity (BAU), which effectively mitigates this effect by maintaining a balance between alignment and uniformity.
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets
Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes.
Deep Video Inpainting Guided by Audio-Visual Self-Supervision
To implement the prior knowledge, we first train the audio-visual network, which learns the correspondence between auditory and visual information.
Towards Content-based Pixel Retrieval in Revisited Oxford and Paris
To this end, we propose pixel retrieval benchmarks named PROxford and PRParis, which are based on the widely used image retrieval datasets, ROxford and RParis.
Pixel-wise Guidance for Utilizing Auxiliary Features in Monte Carlo Denoising
Then we design our ensembling network to obtain per-pixel ensembling weight maps, which represent pixel-wise guidance for which auxiliary feature should be dominant at reconstructing each individual pixel and use them to ensemble the two denoised results of our denosiers.
Feature Separation and Recalibration for Adversarial Robustness
The Separation part disentangles the input feature map into the robust feature with activations that help the model make correct predictions and the non-robust feature with activations that are responsible for model mispredictions upon adversarial attack.
Diverse Generative Perturbations on Attention Space for Transferable Adversarial Attacks
Adversarial attacks with improved transferability - the ability of an adversarial example crafted on a known model to also fool unknown models - have recently received much attention due to their practicality.
Part-based Pseudo Label Refinement for Unsupervised Person Re-identification
In this paper, we propose a novel Part-based Pseudo Label Refinement (PPLR) framework that reduces the label noise by employing the complementary relationship between global and part features.
Ranked #4 on
Unsupervised Vehicle Re-Identification
on VeRi-776
