The challenge in fine-grained visual categorization lies in how to explo...
In recent years, graph neural networks (GNN) have achieved significant
d...
With the fast development of AI-related techniques, the applications of
...
Few-shot class-incremental learning (FSCIL) has recently attracted exten...
Graph neural networks (GNNs) with missing node features have recently
re...
Hashing that projects data into binary codes has shown extraordinary tal...
The key challenge of zero-shot learning (ZSL) is how to infer the latent...
Trajectory prediction aims to predict the movement trend of the agents l...
Learning to understand and predict future motions or behaviors for agent...
Generalized zero-shot learning (GZSL) has achieved significant progress,...
It is essential but challenging to predict future trajectories of variou...
Visual images usually contain the informative context of the environment...
Low-rank Multi-view Subspace Learning (LMvSL) has shown great potential ...
It remains challenging to automatically predict the multi-agent trajecto...
Dynamic texture (DT) exhibits statistical stationarity in the spatial do...
Weakly-supervised semantic segmentation aims to assign each pixel a sema...
High angular resolution diffusion imaging (HARDI) demands a lager amount...
It remains challenging to automatically segment kidneys in clinical
ultr...
It remains challenging to automatically segment kidneys in clinical
ultr...
Despite the great potential of using the low-rank matrix recovery (LRMR)...