Spatial Reasoning
SpatialRGPT: Grounded Spatial Reasoning in Vision-Language Models
Vision Language Models (VLMs) have demonstrated remarkable performance in 2D vision and language tasks. However, their ability to reason about spatial arrangements remains limited. In this work, we introduce Spatial Region GPT (SpatialRGPT) to enhance VLMs' spatial perception and reasoning capabilities.
Synergistic Dual Spatial-aware Generation of Image-to-Text and Text-to-Image Y u Zhao
In the visual spatial understanding (VSU) area, spatial image-to-text (SI2T) and spatial text-to-image (ST2I) are two fundamental tasks that appear in dual form. Existing methods for standalone SI2T or ST2I perform imperfectly in spatial understanding, due to the difficulty of 3D-wise spatial feature modeling.
DreamSteerer: EnhancingSourceImageConditioned EditabilityusingPersonalizedDiffusionModels
However, such a solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods. Specifically, we enhance the source image conditioned editability of a personalized diffusion model via a novel Editability Driven Score Distillation (EDSD) objective. Moreover, we identify amodetrapping issuewithEDSD, andpropose amodeshifting regularization with spatial feature guided sampling to avoid such an issue.
Appendix M
Although freely accessible, it is provided in individual files for each year, and is difficult to combine in order to analyze spatial or temporal trends.
Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actions
In this paper, we introduce a set of spatial-temporal logic rules as knowledge to explain human actions.
Learning from Highly Sparse Spatio-temporal Data
Incomplete spatio-temporal data in the real world has spawned much research.
CYCLO: Cyclic Graph Transformer Approach to Multi-Object Relationship Modeling in Aerial Videos
In this paper, we introduce the new Aero-Eye dataset that focuses on multi-object relationship modeling in aerial videos.
Supplementary Material: T orchSpatial-A Location Encoding Framework and Benchmark for Spatial Representation Learning
Author ordering is determined by coin flip. For what purpose was the dataset created? Was there a specific task in mind? In order to systematically compare the location encoders' performance and their impact on the Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? Dr. Gengchen Mai acknowledges the Microsoft Research What do the instances that comprise the dataset represent (e.g., documents, photos, people, The instances in all 17 datasets represent images.
