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Found 8 papers, 2 papers with code
Segment Any Object Model (SAOM): Real-to-Simulation Fine-Tuning Strategy for Multi-Class Multi-Instance Segmentation
The foundational Segment Anything Model (SAM) is designed for promptable multi-class multi-instance segmentation but tends to output part or sub-part masks in the "everything" mode for various real-world applications.
GenTron: Delving Deep into Diffusion Transformers for Image and Video Generation
In this study, we explore Transformer-based diffusion models for image and video generation.
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editing
In this paper, for the first time, we introduce optical flow into the attention module in the diffusion model's U-Net to address the inconsistency issue for text-to-video editing.
Learning Similarity between Scene Graphs and Images with Transformers
Scene graph generation is conventionally evaluated by (mean) Recall@K, which measures the ratio of correctly predicted triplets that appear in the ground truth.
SSGVS: Semantic Scene Graph-to-Video Synthesis
A semantic scene graph-to-video synthesis framework (SSGVS), based on the pre-trained VSG encoder, VQ-VAE, and auto-regressive Transformer, is proposed to synthesize a video given an initial scene image and a non-fixed number of semantic scene graphs.
RelTR: Relation Transformer for Scene Graph Generation
Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy.
Spatial-Temporal Transformer for Dynamic Scene Graph Generation
Compared to the task of scene graph generation from images, it is more challenging because of the dynamic relationships between objects and the temporal dependencies between frames allowing for a richer semantic interpretation.
