activity
20192026
most citedMonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

2 citations · 2 across the 4 of their papers we have counts for

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cs.CV2026

Unified Multimodal Visual Tracking with Dual Mixture-of-Experts

Lingyi Hong, Jinglun Li, Xinyu Zhou +6

Multimodal visual object tracking can be divided into to several kinds of tasks (e.g. RGB and RGB+X tracking), based on the input modality. Existing methods often train separate mo…

cs.CV2021

Transferring Knowledge with Attention Distillation for Multi-Domain Image-to-Image Translation

Runze Li, Tomaso Fontanini, Luca Donati +2

Gradient-based attention modeling has been used widely as a way to visualize and understand convolutional neural networks. However, exploiting these visual explanations during the…

cs.CV20212 cited

MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments

Pan Ji, Runze Li, Bir Bhanu +1

Self-supervised depth estimation for indoor environments is more challenging than its outdoor counterpart in at least the following two aspects: (i) the depth range of indoor seque…

cs.CV2021

Learning Local Recurrent Models for Human Mesh Recovery

Runze Li, Srikrishna Karanam, Ren Li +3

We consider the problem of estimating frame-level full human body meshes given a video of a person with natural motion dynamics. While much progress in this field has been in singl…

cs.CV2019

Towards Visually Explaining Variational Autoencoders

Wenqian Liu, Runze Li, Meng Zheng +5

Recent advances in Convolutional Neural Network (CNN) model interpretability have led to impressive progress in visualizing and understanding model predictions. In particular, grad…