Most existing animal pose and shape estimation approaches reconstruct animal meshes with a parametric SMAL model. This is because the low-dimensional pose and shape parameters of the SMAL model makes it easier for deep networks to learn the high-dimensional animal meshes. However, the SMAL model is learned from scans of toy animals with limited pose and shape variations, and thus may not be able to represent highly varying real animals well. This may result in poor fittings of the estimated meshes to the 2D evidences, e.g.

The debate over the risks and harms of artificial intelligence often focuses on what governments can or should do. However, just as important are the choices that AI researchers themselves make. This week, in Singapore, more than 100 scientists from around the world proposed guidelines for how researchers should approach making AI more "trustworthy, reliable, and secure." The recommendations come at a time when the giants of generative AI, such as OpenAI and Google, have increasingly reduced disclosures about their AI models, so the public knows less and less about how the work is conducted. The guidelines grew out of an exchange among the scholars last month in Singapore, in conjunction with one of the most prestigious conferences on AI, the International Conference on Learning Representations -- the first time a major AI conference has taken place in Asia.

The government of Singapore released a blueprint today for global collaboration on artificial intelligence safety following a meeting of AI researchers from the US, China, and Europe. The document lays out a shared vision for working on AI safety through international cooperation rather than competition. "Singapore is one of the few countries on the planet that gets along well with both East and West," says Max Tegmark, a scientist at MIT who helped convene the meeting of AI luminaries last month. "They know that they're not going to build [artificial general intelligence] themselves--they will have it done to them--so it is very much in their interests to have the countries that are going to build it talk to each other." The countries thought most likely to build AGI are, of course, the US and China--and yet those nations seem more intent on outmaneuvering each other than working together.

One of the most important annual events in the field of artificial intelligence kicks off this week in Singapore: the International Conference on Learning Representations. As usual, chip giant Nvidia had a major presence at the conference, presenting over 70 research papers from its team. The papers cover topics ranging from generating music to creating 3D-realistic videos, robot training tasks, and the ability to generate multiple large language models at the push of a button. "People often think of Nvidia as a chip company that makes awesome chips, and of course, we're really proud of that," said Bryan Catanzaro, Nvidia's head of applied deep learning research, in an interview with ZDNET. "But the story that I think matters the most is that in order for us to make those awesome chips, we have to do research like this, because this teaches us how to make all of those systems."

"The Big Weekend Show" analyzes the possibilities of artificial intelligence when it comes to influencing voters. If you haven't heard the buzz about Manus yet, it's the new AI model unveiled by a Singapore-based company called Butterfly Effect. It's one of the first truly autonomous AI agents, able to do its own research, make decisions and even carry out plans, all with barely any human oversight. But here's the thing: While all this innovation opens up exciting possibilities, it also brings some serious privacy and security questions. Whether you're eager to try out the latest AI or you'd rather steer clear, it's worth understanding what Manus could mean for your personal data and digital safety.

Scene graph generation (SGG) analyzes images to extract meaningful information about objects and their relationships. In the dynamic visual world, it is crucial for AI systems to continuously detect new objects and establish their relationships with existing ones. Recently, numerous studies have focused on continual learning within the domains of object detection and image recognition. However, a limited amount of research focuses on a more challenging continual learning problem in SGG. This increased difficulty arises from the intricate interactions and dynamic relationships among objects, and their associated contexts. Thus, in continual learning, SGG models are often required to expand, modify, retain, and reason scene graphs within the process of adaptive visual scene understanding.

The key challenge of multi-view indoor 3D object detection is to infer accurate geometry information from images for precise 3D detection. Previous method relies on NeRF for geometry reasoning. However, the geometry extracted from NeRF is generally inaccurate, which leads to sub-optimal detection performance. In this paper, we propose MVSDet which utilizes plane sweep for geometry-aware 3D object detection. To circumvent the requirement for a large number of depth planes for accurate depth prediction, we design a probabilistic sampling and soft weighting mechanism to decide the placement of pixel features on the 3D volume. We select multiple locations that score top in the probability volume for each pixel and use their probability score to indicate the confidence. We further apply recent pixel-aligned Gaussian Splatting to regularize depth prediction and improve detection performance with little computation overhead. Extensive experiments on ScanNet and ARKitScenes datasets are conducted to show the superiority of our model.

Local Bayesian optimization is a promising practical approach to solve high dimensional black-box function optimization problem. Among them is the approximated gradient class of methods, which implements a strategy similar to gradient descent. These methods have achieved good experimental results and theoretical guarantees.

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent.