Titlebook: Information and Communication Technology and the Teacher of the Future; IFIP TC3 / WG3.1 & W Carolyn Dowling,Kwok-Wing Lai Book 2003 IFIP I

指派

o inputs, following textual edit prompts. However, such solutions typically incur heavy memory and computational costs to generate temporally-coherent frames, either in the form of diffusion inversion and/or cross-frame attention. In this paper, we conduct an analysis of such inefficiencies, and sug

Nmda-Receptor

圆木可阻碍

Barry Harperention by learning multiple click prompts to generate corresponding prompt-activated masks, and selecting one from these masks. However, directly matching each prompt to the same visual feature often leads to homogeneous prompt-activated masks, as it pushes the click prompts to converge to one point

Outwit

Deryn Watson able to generate agent bounding boxes and lane graphs. The model’s outputs serve as an initial state for rule-based traffic simulation. The unique properties of the entities to be generated for SLEDGE, such as their connectivity and variable count per scene, render the naive application of most mod

名词

sually employ neural networks and volumetric rendering, which are costly to train and impede their broad use in various real-time applications due to the lengthy rendering time. Lately 3D Gaussians splatting-based approach has been proposed to model the 3D scene, and it achieves remarkable visual qu

Optic-Disk

Rosa Maria Bottinoel annotations for training. To address this issue, recent advances explore an efficient one-stage weakly supervised REC model called RefCLIP. Particularly, RefCLIP utilizes anchor features of pre-trained one-stage detection networks to represent candidate objects and conducts anchor-text ranking to

frenzy

Roger Carlsenganize the neural radiance field. Existing object-centric methods focus only on the inherent characteristics of objects, while overlooking the semantic and physical relationships between them. Our scene graph is adept at managing the complex real-world correlation between objects within a scene, ena

Mumble

Niki Davis,Mari Kemis,Natalie Johnsonting approaches rely on modality-invariant features to alleviate this issue but ignore modality-specific features. To solve this issue, we propose a .issing .odality .dapter framework for .ace .nti-.poofing (MMA-FAS), which leverages modality-disentangle adapters and LBP-guided contrastive loss for

FLING

Anthony Joneshis paper, we reveal that existing methods still face significant challenges in generating images that align with the image conditional controls. To this end, we propose ControlNet++, a novel approach that improves controllable generation by explicitly optimizing pixel-level cycle consistency betwee

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