During the inference, we set the background score as 0.95.

This paper studies the problem of weakly open-vocabulary semantic segmentation (WOVSS), which learns to segment objects of arbitrary classes using mere image-text pairs. Existing works turn to enhance the vanilla vision transformer by introducing explicit grouping recognition, i.e., employing several group tokens/centroids to cluster the image tokens and perform the group-text alignment. Nevertheless, these methods suffer from a granularity inconsistency regarding the usage of group tokens, which are aligned in the all-to-one v.s.

Large language models (LLMs) have demonstrated impressive capabilities in various tasks using the in-context learning (ICL) paradigm.

The latter usually defines the protein's key function, but cannot

However, local attention limits the receptive field to one-hop neighbors.

Race for AI is making Hindenburg-style disaster'a real risk', says leading expert The race to get artificial intelligence to market has raised the risk of a Hindenburg-style disaster that shatters global confidence in the technology, a leading researcher has warned. Michael Wooldridge, a professor of AI at Oxford University, said the danger arose from the immense commercial pressures that technology firms were under to release new AI tools, with companies desperate to win customers before the products' capabilities and potential flaws are fully understood. The surge in AI chatbots with guardrails that are easily bypassed showed how commercial incentives were prioritised over more cautious development and safety testing, he said. "It's the classic technology scenario," he said. "You've got a technology that's very, very promising, but not as rigorously tested as you would like it to be, and the commercial pressure behind it is unbearable."

"Effective robustness" measures the extra out-of-distribution (OOD) robustness