It maps the discrete behavior data into a continuous latent space, and generates behaviors with the guidance of collaborative signals and user multimodal preference.
To be specific, in the proposed MSGNN model, we first devise a novel graph learning module, which directly captures long-range connectivity from trans-regional epidemic signals and integrates them into a multi-scale graph.
Meanwhile, a behavior-aware fuser is designed to comprehensively model user preferences by adaptively learning the relative importance of different modality features.
The complex scene understanding ability of CLIP enables the discriminator to accurately assess the image quality.
Ranked #3 on Text-to-Image Generation on CUB
Recently, Multiple Object Tracking has achieved great success, which consists of object detection, feature embedding, and identity association.
To solve these limitations, we propose: (i) a Dynamic Editing Block (DEBlock) which composes different editing modules dynamically for various editing requirements.
However, existing graph-based methods fail to perform multi-step reasoning well, neglecting two properties of VideoQA: (1) Even for the same video, different questions may require different amount of video clips or objects to infer the answer with relational reasoning; (2) During reasoning, appearance and motion features have complicated interdependence which are correlated and complementary to each other.
Ranked #27 on Visual Question Answering (VQA) on MSRVTT-QA
To these ends, we propose a simpler but more effective Deep Fusion Generative Adversarial Networks (DF-GAN).
Ranked #4 on Text-to-Image Generation on CUB (Inception score metric)