Despite remarkable advances in image synthesis research, existing works often fail in manipulating images under the context of large geometric transformations. Synthesizing person images conditioned on arbitrary poses is one of the most representative examples where the generation quality largely relies on the capability of identifying and modeling arbitrary transformations on different body parts. Current generative models are often built on local convolutions and overlook the key challenges (e.g.

Achieving machine autonomy and human control often represent divergent objectives in the design of interactive AI systems.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

It has been observed that visual classification models often rely mostly on spurious cues such as the image background, which hurts their robustness to distribution changes. To alleviate this shortcoming, we propose to monitor the model's relevancy signal and direct the model to base its prediction on the foregroundobject.

Foundation models have emerged as powerful tools across various domains including language, vision, and multimodal tasks.

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology.