259 citations · 325 across the 25 of their papers we have counts for
14 papers · 1 filter
Weakly-supervised Action Transition Learning for Stochastic Human Motion Prediction
Wei Mao, Miaomiao Liu, Mathieu Salzmann
We introduce the task of action-driven stochastic human motion prediction, which aims to predict multiple plausible future motions given a sequence of action labels and a short mot…
MulT: An End-to-End Multitask Learning Transformer
Deblina Bhattacharjee, Tong Zhang, Sabine Süsstrunk +1
We propose an end-to-end Multitask Learning Transformer framework, named MulT, to simultaneously learn multiple high-level vision tasks, including depth estimation, semantic segmen…
Leverage Your Local and Global Representations: A New Self-Supervised Learning Strategy
Tong Zhang, Congpei Qiu, Wei Ke +2
Self-supervised learning (SSL) methods aim to learn view-invariant representations by maximizing the similarity between the features extracted from different crops of the same imag…
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
Van Nguyen Nguyen, Yinlin Hu, Yang Xiao +2
We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these obj…
Deep Action- and Context-Aware Sequence Learning for Activity Recognition and Anticipation
Mohammad Sadegh Aliakbarian, Fatemehsadat Saleh, Basura Fernando +3
Action recognition and anticipation are key to the success of many computer vision applications. Existing methods can roughly be grouped into those that extract global, context-awa…
Efficient Linear Programming for Dense CRFs
Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel +3
The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a d…