Ukrainian President Volodymyr Zelensky is set to visit Prime Minister Sir Keir Starmer in the UK on Tuesday to agree a new defence partnership aimed at tackling cheap attack drones. Downing Street said the deal would bring together Ukrainian expertise and the UK's industrial base to manufacture and supply drones and other capabilities. The two leaders are also expected to discuss further support Ukraine against Russia's full-scale invasion, now in its fourth year. Their meeting comes as the US-Israeli war with Iran enters a third week, during which US President Donald Trump has criticised the UK and other countries over the extent of their response to the conflict. Under the partnership between the UK and Ukraine, closer co-operation in the defence industries will also be sought with third countries as part of efforts to bolster international security.

Trump says Putin may be helping Iran'a bit' US President Donald Trump has said he believes that Vladimir Putin and Russia are helping Iran a bit amid the conflict with the US and Israel. In an interview with Fox News, Trump acknowledged that the US also helps Ukrainian forces as they battle with Russian forces. According to some US media reports, Russia has been sharing the location of US military forces with Iran that could help guide missile and drone attacks across the Middle East. On Thursday, US Special Envoy for the Middle East Steve Witkoff said that Russia's government had assured the Trump administration that it was not providing intelligence to the Iranian government in Tehran. Asked by Fox about the potential of Russian intelligence being shared with Iran, Trump said that I think he [Putin] may be helping them a bit, yeah.

In this paper, we introduce the CityRefer dataset for city-level 3D visual grounding.

Novel categories names:['High_Jump', 'Front_Crawl', 'Pole_V ault', 'Hammer_Throw', All experiments are conducted under the 16-shot setting. An incremental bayesian approach tested on 101 object categories. Conditional prompt learning for vision-language models.

Advances in in computer vision have lead to classifiers that nearly match human performance in many applications. However, while the human visual system is remarkably versatile in extracting semantic meaning out of even degraded and heavily obfuscated images, today's visual classifiers significantly lag behind in emulating the same robustness, and often yield incorrect outputs in the presence of natural and adversarial degradations.

While the ImageNet dataset has been driving computer vision research over the past decade, significant label noise and ambiguity have made top-1 accuracy an insufficient measure of further progress.