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Found 12 papers, 4 papers with code
Video Diffusion Models: A Survey
This survey offers a systematic overview of critical elements of diffusion models for video generation, covering applications, architectural choices, and the modeling of temporal dynamics.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
Joint Generative Modeling of Scene Graphs and Images via Diffusion Models
While previous works have explored image generation conditioned on scene graphs or layouts, our task is distinctive and important as it involves generating scene graphs themselves unconditionally from noise, enabling efficient and interpretable control for image generation.
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval
Following this, the chosen articles are subjected to zero-shot summarization to attain succinct context.
SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation
Diffusion models based on permutation-equivariant networks can learn permutation-invariant distributions for graph data.
What Constitutes Good Contrastive Learning in Time-Series Forecasting?
Despite these advances, there remains a significant gap in understanding the impact of different SSCL strategies on time series forecasting performance, as well as the specific benefits that SSCL can bring.
Hierarchically Constrained Adaptive Ad Exposure in Feeds
To this end, adaptive ad exposure has become an appealing strategy to boost the overall performance of the feed.
CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data
We present a visual localization system that learns to estimate camera poses in the real world with the help of synthetic data.
Social NCE: Contrastive Learning of Socially-aware Motion Representations
Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds.
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Trajectory Prediction
on TrajNet++
Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions
Optimal selection of which teammates a robot should take a relative measurement from such that the updated joint localization uncertainty of the team is minimized is an NP-hard problem.
Robotics
Revealing Fine Structures of the Retinal Receptive Field by Deep Learning Networks
When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to higher visual cortex.
Revealing structure components of the retina by deep learning networks
By training CNNs with white noise images to predicate neural responses, we found that convolutional filters learned in the end are resembling to biological components of the retinal circuit.
