This work proposes a general method for learning task and scale based attention representations in Multi-Task Learning (MTL) for vision that adds an attention modulated feature as residual information to the processing of each scale stage within the model, including the final layer of task outputs.
This work proposes MSGAN, a multi-scale global-local collaborative learning framework that integrates multi-scale mixed convolution and adaptive global-local attention to enhance feature representation and advances robust SOD for complex real-world scenarios and provides insights into attention-guided visual perception...
The study provides an empirical framework for characterizing scale robustness across vision architectures and identifies a reproducible accuracy-scale regularity that warrants further theoretical investigation.
: Fine-grained visual categorization (FGVC) presents a class of recognition problems in which the discriminative signal is spatially concentrated, visually subtle, and easily destroyed by the preprocessing and augmentation strategies that serve coarse recognition well. Where standard image classification requires a mod...
Richard Adusei, G. Abdul-Salaam· Journal of Artificial Intell...· 0 citations
Asynchronous Learning Rate is proposed, which assigns depth-dependent initial learning rates to network modules according to their topological depth, and Smoothed Synchronous Decay is proposed, which coordinates the subsequent decay of heterogeneous parameter groups.
Qiang He, Qiu Zong, Yi-Qi Wang et al.· IEEE Access· 0 citations
Results show that pretrained convolutional representations, when integrated through learned adaptation and fusion, can improve the visual understanding of existing VLMs without replacing their original visual encoders.
MaskVLA, a masking-based fine-tuning strategy that randomly masking a small portion of the main camera's visual information leads to the emergence of robust policies, thereby enhancing the model's capability to tackle complex manipulation tasks and improving its generalization performance.
Yuxuan Jiang, Jia-Ying Huang, Ge Wang et al.· 0 citations
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