Summary of Trajectory Forecasting Through Low-rank Adaptation Of Discrete Latent Codes, by Riccardo Benaglia et al.
Trajectory Forecasting through Low-Rank Adaptation of Discrete Latent Codes
by Riccardo Benaglia, Angelo Porrello, Pietro Buzzega, Simone Calderara, Rita Cucchiara
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to trajectory forecasting in video surveillance analytics using Vector Quantized Variational Autoencoders (VQ-VAEs). The method leverages a discrete latent space to tackle the issue of posterior collapse and achieves an optimal balance between sampling fidelity and diversity. The instance-based codebook is dynamically adjusted based on contextual information from past motion patterns, leading to improved reconstructions. The framework combines VQ-VAE with diffusion-based predictive modeling for accurate and diverse forecasts, outperforming state-of-the-art methods on three established benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how we can predict where people or objects will move in the future based on what has happened before. This is important for video surveillance analytics because it allows us to anticipate movements and make predictions about what might happen next. The researchers used a special type of AI model called VQ-VAE, which helps to balance the accuracy and diversity of their predictions. They also developed a new way to adjust this model based on the context of what has happened before, making their predictions even more accurate. |
Keywords
» Artificial intelligence » Diffusion » Latent space
