Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However, the two training flows are separate, which allows labeled data to dominate the training process, resulting in low-quality pseudo labels and, consequently, sub-optimal results. To alleviate this issue, we present AllSpark, which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level, which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal, Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark.

Text-driven 3D scene generation techniques have made rapid progress in recent years. Their success is mainly attributed to using existing generative models to iteratively perform image warping and inpainting to generate 3D scenes. However, these methods heavily rely on the outputs of existing models, leading to error accumulation in geometry and appearance that prevent the models from being used in various scenarios (e.g., outdoor and unreal scenarios). To address this limitation, we generatively refine the newly generated local views by querying and aggregating global 3D information, and then progressively generate the 3D scene. Specifically, we employ a tri-plane features-based NeRF as a unified representation of the 3D scene to constrain global 3D consistency, and propose a generative refinement network to synthesize new contents with higher quality by exploiting the natural image prior from 2D diffusion model as well as the global 3D information of the current scene. Our extensive experiments demonstrate that, in comparison to previous methods, our approach supports wide variety of scene generation and arbitrary camera trajectories with improved visual quality and 3D consistency.

This study examines the use of Large Language Models (LLMs) for retrieving factual information, addressing concerns over their propensity to produce factually incorrect "hallucinated" responses or to altogether decline to even answer prompt at all. Specifically, it investigates the presence of gender-based biases in LLMs' responses to factual inquiries. This paper takes a multi-pronged approach to evaluating GPT models by evaluating fairness across multiple dimensions of recall, hallucinations and declinations. Our findings reveal discernible gender disparities in the responses generated by GPT-3.5. While advancements in GPT-4 have led to improvements in performance, they have not fully eradicated these gender disparities, notably in instances where responses are declined. The study further explores the origins of these disparities by examining the influence of gender associations in prompts and the homogeneity in the responses.

This election cycle will be the first exposed to generative artificial intelligence--the technology behind popular apps like ChatGPT that enables even non-experts to create fake, but realistic-looking text, video, and audio perfectly suited for political manipulation. At the same time, a number of the major social-media companies have retreated from some of their prior commitments to promote "election integrity." The November election is also the first that will register the impact of the enormous popularity of TikTok, which uses a recommendation algorithm that some experts believe is particularly suited to spreading misinformation. Let's start with the rise of generative AI, which allows virtually anyone to produce persuasive text, imagery, or sound based on relatively simple natural-language prompts. In January, Facebook circulated a fake AI-generated image of Donald Trump sitting next to Jeffrey Epstein on the disgraced financier and sex offender's private jet.

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a "relevance topic model" for jointly learning meaningful mid-level representations upon bagof-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i.e., Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos.

Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this approach suffers from the cold start problem: it fails when no usage data is available, so it is not effective for recommending new and unpopular songs. In this paper, we propose to use a latent factor model for recommendation, and predict the latent factors from music audio when they cannot be obtained from usage data. We compare a traditional approach using a bag-of-words representation of the audio signals with deep convolutional neural networks, and evaluate the predictions quantitatively and qualitatively on the Million Song Dataset. We show that using predicted latent factors produces sensible recommendations, despite the fact that there is a large semantic gap between the characteristics of a song that affect user preference and the corresponding audio signal. We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.

With classic oblivious timing, Nintendo chose 10 March – or Mar10 day, as the company likes to style it – to announce that it is working with Illumination Studios on another Mario movie, even though it was the Oscars that day and absolutely nobody was paying attention. Last year's Mario movie was a smash hit, grossing 1bn and finally ending the long era of the cursed video game film adaptation once and for all, so it's not surprising that another one is in the works for April 2026. What is surprising is that it's not necessarily going to be a direct sequel. Co-directors Aaron Horvath and Michael Jelenic and writer Matthew Fogel will return, but neither Nintendo nor Illumination committed to calling the new film a sequel. In a video broadcast announcing "a new animated film based on the world of Super Mario Bros", Nintendo's Shigeru Miyamoto (that's Mario's dad) said: "This time, we're thinking about broadening Mario's world further, and it'll have a bright and fun story."

Artificial Intelligence words are seen in this illustration taken on March 31, 2023. RISE OF THE MACHINES: The U.S. government has a "clear and urgent need" to act, as swiftly developing artificial intelligence could potentially lead to human extinction through weaponization and loss of control, according to a government-commissioned report. 'SMALL, SMART, CHEAP': The Pentagon will look to develop new artificial intelligence-guided planes, offering two contracts that several private companies have been competing to obtain. The Pentagon is seen from a flight taking off from Ronald Reagan Washington National Airport in Arlington, Virginia. While this technology offers many astonishing benefits, it also poses significant dangers.

We consider the problem of embedding entities and relationships of multirelational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.

There aren't enough films that put movie stars in mechs and task them with saving the world. But that's just what Netflix flick Atlas is doing with Jennifer Lopez. She plays Atlas Shepherd, "a brilliant but misanthropic data analyst with a deep distrust of artificial intelligence," who joins a team that's aiming to secure a renegade robot. As it happens, Atlas and said machine share "a mysterious past," according to Netflix. Inevitably, things don't go as planned.