Scene understanding is a crucial aspect of computer vision that involves not only identifying objects in a scene but also understanding their relationships and context. This article explores recent advancements in scene understanding, focusing on the challenges and applications of this technology. Scene understanding has been a topic of interest in various research studies, with many focusing on single scenes or groups of adjacent scenes. However, the semantic similarity between different but related scenes is not generally exploited to improve automated surveillance tasks and reduce manual effort. To address these challenges, researchers have developed frameworks for distributed multiple-scene global understanding that cluster surveillance scenes based on their ability to explain each other's behaviors and discover shared activities. Recent advancements in deep learning have significantly improved scene understanding, particularly in robotics applications. By incorporating object-level information and using regularization of semantic segmentation, deep learning architectures have achieved superior scene classification results on publicly available datasets. Additionally, researchers have proposed methods for learning 3D semantic scene graphs from 3D indoor reconstructions, which can be used for domain-agnostic retrieval tasks and 2D-3D matching. Practical applications of scene understanding include: 1. Surveillance: Improved scene understanding can enhance the effectiveness of surveillance systems by automatically analyzing and summarizing video data, reducing the need for manual monitoring. 2. Robotics: Scene understanding can help robots navigate and interact with their environments more effectively, enabling them to perform tasks such as object recognition, navigation, and manipulation. 3. Autonomous vehicles: Scene understanding can improve the safety and efficiency of autonomous vehicles by enabling them to better interpret and respond to their surroundings. One company case study involves a proposed method for automotive foggy scene understanding via domain adaptation to an illumination-invariant representation. This method employs domain transfer and a competitive encoder-decoder convolutional neural network (CNN) to achieve state-of-the-art performance in automotive scene understanding under foggy weather conditions. In conclusion, scene understanding is a vital aspect of computer vision that has seen significant advancements in recent years. By leveraging deep learning techniques and incorporating object-level information, researchers have developed innovative methods for improving scene understanding in various applications, such as surveillance, robotics, and autonomous vehicles. As the field continues to evolve, it is expected that scene understanding will play an increasingly important role in the development of intelligent systems.
Scheduled Sampling
What is scheduled sampling?
Scheduled sampling is a technique used in sequence generation problems, particularly in auto-regressive models, to improve the performance of machine learning models by mitigating discrepancies between training and testing phases. It addresses the issue of teacher-forcing by randomly replacing some discrete units in the input history with the model's prediction, bridging the gap between training and testing conditions.
Why was scheduled sampling introduced?
Scheduled sampling was introduced to address the discrepancies between training and testing phases in sequence generation problems. During training, auto-regressive models use teacher-forcing, where the ground-truth history is provided as input. However, at test time, the ground-truth is replaced by the model's prediction, leading to a mismatch between training and testing conditions. Scheduled sampling helps to reduce this mismatch and improve the model's performance.
What is teacher forcing in deep learning?
Teacher forcing is a technique used in training auto-regressive models, where the ground-truth history is provided as input to the model during the training phase. This approach helps the model learn the correct output sequence by using the actual data as a guide. However, it can lead to discrepancies between training and testing conditions, as the ground-truth history is not available during testing.
How does scheduled sampling improve sequence generation?
Scheduled sampling improves sequence generation by gradually exposing the model to its own predictions during training. By randomly replacing some discrete units in the input history with the model's prediction, the model learns to generate sequences more accurately under testing conditions, where it must rely on its own predictions instead of the ground-truth history.
What are some recent advancements in scheduled sampling research?
Recent research in scheduled sampling has focused on parallelization, optimization of annealing schedules, and reinforcement learning for efficient scheduling. Parallel Scheduled Sampling enables parallelization across time, leading to improved performance in tasks like image generation and dialog response generation. Optimal annealing schedules have been proposed to outperform conventional scheduling schemes. Symphony, a scheduling framework, leverages domain-driven Bayesian reinforcement learning and a sampling-based technique to reduce training data and time requirements, resulting in better scheduling policies.
What are some practical applications of scheduled sampling?
Practical applications of scheduled sampling can be found in various domains, such as image generation, natural language processing tasks like dialog response generation and translation, and optimization of scheduling in multi-source systems. In image generation, it has led to significant improvements in Frechet Inception Distance (FID) and Inception Score (IS). In natural language processing tasks, it has resulted in higher BLEU scores.
Can you provide a case study of a company using scheduled sampling?
One company case study involves Symphony, which uses a domain-driven Bayesian reinforcement learning model for scheduling and a sampling-based technique to compute gradients. This approach reduces both the amount of training data and the time required to produce scheduling policies, significantly outperforming black-box approaches.
Scheduled Sampling Further Reading
1.AutoSampling: Search for Effective Data Sampling Schedules http://arxiv.org/abs/2105.13695v1 Ming Sun, Haoxuan Dou, Baopu Li, Lei Cui, Junjie Yan, Wanli Ouyang2.REX: Revisiting Budgeted Training with an Improved Schedule http://arxiv.org/abs/2107.04197v1 John Chen, Cameron Wolfe, Anastasios Kyrillidis3.Parallel Scheduled Sampling http://arxiv.org/abs/1906.04331v2 Daniel Duckworth, Arvind Neelakantan, Ben Goodrich, Lukasz Kaiser, Samy Bengio4.Bilateral Teleoperation of Multiple Robots under Scheduling Communication http://arxiv.org/abs/1804.04290v1 Yuling Li, Kun Liu, Wei He, Yixin Yin, Rolf Johansson, Kai Zhang5.Variational Optimization of Annealing Schedules http://arxiv.org/abs/1502.05313v2 Taichi Kiwaki6.Feedback Scheduling of Priority-Driven Control Networks http://arxiv.org/abs/0806.0130v1 Feng Xia, Youxian Sun, Yu-Chu Tian7.Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters http://arxiv.org/abs/1909.02119v2 Subho S Banerjee, Saurabh Jha, Zbigniew T. Kalbarczyk, Ravishankar K. Iyer8.Age-optimal Sampling and Transmission Scheduling in Multi-Source Systems http://arxiv.org/abs/1812.09463v3 Ahmed M. Bedewy, Yin Sun, Sastry Kompella, Ness B. Shroff9.Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness http://arxiv.org/abs/2004.00490v2 Jinke Ren, Yinghui He, Dingzhu Wen, Guanding Yu, Kaibin Huang, Dongning Guo10.Smart Sampling for Lightweight Verification of Markov Decision Processes http://arxiv.org/abs/1409.2116v2 Pedro D'Argenio, Axel Legay, Sean Sedwards, Louis-Marie TraonouezExplore More Machine Learning Terms & Concepts
Scene Understanding Score Matching Score Matching: A powerful technique for learning high-dimensional density models in machine learning. Score matching is a recently developed method in machine learning that is particularly effective for learning high-dimensional density models with intractable partition functions. It has gained popularity due to its robustness with noisy training data and its ability to handle complex models and high-dimensional data. This article delves into the nuances, complexities, and current challenges of score matching, providing expert insight and discussing recent research and future directions. One of the main challenges in score matching is the difficulty of computing the Hessian of log-density functions, which has limited its application to simple, shallow models or low-dimensional data. To overcome this issue, researchers have proposed sliced score matching, which involves projecting the scores onto random vectors before comparing them. This approach only requires Hessian-vector products, making it more suitable for complex models and higher-dimensional data. Recent research has also explored the relationship between maximum likelihood and score matching, showing that matching the first-order score is not sufficient to maximize the likelihood of the ODE (Ordinary Differential Equation). To address this, a novel high-order denoising score matching method has been developed, enabling maximum likelihood training of score-based diffusion ODEs. In addition to these advancements, researchers have proposed various extensions and generalizations of score matching, such as neural score matching for high-dimensional causal inference and generalized score matching for regression. These methods aim to improve the applicability and performance of score matching in different settings and data types. Practical applications of score matching can be found in various domains, such as: 1. Density estimation: Score matching can be used to learn deep energy-based models effectively, providing accurate density estimates for complex data distributions. 2. Causal inference: Neural score matching has been shown to be competitive against other matching approaches for high-dimensional causal inference, both in terms of treatment effect estimation and reducing imbalance. 3. Graphical model estimation: Regularized score matching has been used to estimate undirected conditional independence graphs in high-dimensional settings, achieving state-of-the-art performance in Gaussian cases and providing a valuable tool for non-Gaussian graphical models. A company case study showcasing the use of score matching is OpenAI, which has developed a method called Concrete Score Matching (CSM) for modeling discrete data. CSM generalizes score matching to discrete settings by defining a novel score function called the 'Concrete score'. Empirically, CSM has demonstrated efficacy in density estimation tasks on a mixture of synthetic, tabular, and high-dimensional image datasets, performing favorably compared to existing baselines. In conclusion, score matching is a powerful technique in machine learning that has seen significant advancements and generalizations in recent years. By connecting to broader theories and overcoming current challenges, score matching has the potential to become an even more versatile and effective tool for learning high-dimensional density models across various domains and applications.
