Understanding the semantics of individual regions or patches of unconstrained images, such as open-world object detection, remains a critical yet challenging task in computer vision. Building on the success of powerful image-level vision-language (ViL) foundation models like CLIP, recent efforts have sought to harness their capabilities by either training a contrastive model from scratch with an extensive collection of region-label pairs or aligning the outputs of a detection model with image-level representations of region proposals. Despite notable progress, these approaches are plagued by computationally intensive training requirements, susceptibility to data noise, and deficiency in contextual information. To address these limitations, we explore the synergistic potential of off-the-shelf foundation models, leveraging their respective strengths in localization and semantics. We introduce a novel, generic, and efficient architecture, named RegionSpot, designed to integrate position-aware localization knowledge from a localization foundation model (e.g., SAM) with semantic information from a ViL model (e.g., CLIP).

Apple's recent flurry of software updates also includes a big upgrade for the living room. The newly released tvOS 16.2 adds a Recognize My Voice feature that customizes Siri searches on the Apple TV 4K and TV HD for up to six family members. Once you've trained the set-top to know who's speaking, you can ask for video recommendations and music without worrying that you'll mess with someone's play history. You can also ask to "switch to my profile" instead of navigating the on-screen switcher. You can also change the Siri language to be different than the one your device shows.

Despite constant movements of the body, head or eyes, our visual perception of the objects around us remains stable even though the physical information hitting our retinas is constantly changing. Scientists at the RIKEN Institute in Japan have looked at all the unnoticed eye movements we make and have shown that they allow us to recognize objects in a stable way. These findings can be applied to computer vision and be particularly useful for autonomous driving systems. They published their study entitled " Motor-related signals support localization invariance for stable visual perception"in the scientific journal PLOS Computational Biology. RIKEN, the largest global research institution in Japan, is internationally recognized for its high-quality research in a wide range of scientific disciplines.

The quest to give machines human-level intelligence has been around for decades, and it has captured imaginations for far longer -- think of Mary Shelley's Frankenstein in the 19th century. Artificial intelligence, or AI, was born in the 1950s, with boom cycles leading to busts as scientists failed time and again to make machines act and think like the human brain. But this time could be different because of a major breakthrough -- deep learning, where data structures are set up like the brain's neural network to let computers learn on their own. Together with advances in computing power and scale, AI is making big strides today like never before. Frank Chen, a partner specializing in AI at top venture capital firm Andreessen Horowitz, makes a case that AI could be entering a golden age.

Sloman was one of the first in the AI community to write about the role of emotion in computing (Sloman and Croucher 1981), and I value his insight into theories of emotional and intelligent systems. Alas, Sloman's review dwells largely on some details related to unknown features of human emotion; hence, I don't think the review captures the flavor of the book. However, he does raise interesting points, as well as potential misunderstandings, both of which I am grateful for the opportunity to comment on. Sloman writes that I "welcome emotion detectors in a wide range of contexts and relationships, for example, teacher and pupil." This might sound innocuous, but its presumption of the existence of emotion detectors is not.

Planning domains often feature subproblems such as route planning and resource handling. Using static domain analysis techniques, we have been able to identify certain commonly occurring subproblems within planning domains, making it possible to abstract these subproblems from the overall goals of the planner and deploy specialized technology to handle them in a way integrated with the broader planning activities. Although such strategies can be impressive when applied to toy domains, they cannot address highly structured problem domains effectively. However, when knowledge-sparse approaches are supplemented by domain knowledge, they can perform impressively (Bacchus and Kabanza 2000) at the cost of an increased representation burden on the domain designer.

As evidenced by the articles in this special issue, transfer learning has come a long way in the past five or so years, partially because of DARPA's Transfer Learning program, which sponsored much of the work reported in this issue. There is a Transfer Learning Toolkit for Matlab available on the web. Transfer learning has developed techniques for classification, regression, and clustering (as summarized in Pan and Yang's 2009 survey) and for complex interactive tasks that are often best addressed by reinforcement learning techniques. However, there is a more practical and more feasible goal for transfer learning against which progress is being made. An engineering-oriented goal of artificial intelligence that could be enabled by transfer learning is the ability to construct a large number of diverse applications not from scratch, but by taking advantage of knowledge already acquired and formally represented for other purposes.

Artificial intelligence, or AI, allows computer systems to automatically recognize and perform certain jobs that formerly would have required human intervention. If you've ever loaded a new image into the photos application on your computer and had it instantly recognize the faces of every person there, you've seen the power of AI on display. Machine learning, on the other hand, takes things one step farther and allows computer systems to essentially learn and improve from experience -- without necessarily being programmed to do so. Using the same example as above, say you load an image into the photos app and tag a photo of yourself and your significant other. When you load another photo featuring the two of you into the app a few weeks later, it will nstantly recognize you and display your names -- without you doing anything manually.

Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn't look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn't follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it. Getting a car to drive this way was an impressive feat.

Unlike task-specific algorithms, Deep Learning is a part of Machine Learning family based on learning data representations. With massive amounts of computational power, machines can now recognize objects and translate speech in real time, enabling a smart Artificial intelligence in systems. The concept of a software simulating the neocortex's large array of neurons in an artificial neural network is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, today researchers & data scientists can model many more layers of virtual neurons than ever before. Languishing through the 1970's, early neural networks could simulate only a very limited number of neurons at once, so they could not recognize patterns of great complexity.