Lip reading aims at transforming the videos of continuous lip movement into textual contents, and has achieved significant progress over the past decade. It serves as a critical yet practical assistance for speech-impaired individuals, with more practicability than speech recognition in noisy environments.

Ukraine prepared to fight North Korean troops in the Russian region of Kursk on Wednesday, as the entry of a second nuclear power in Russia's war against Ukraine threatened to escalate and broaden the conflict. The United States Pentagon confirmed on Tuesday that North Korean troops were in Kursk, where Ukraine launched a counter-invasion almost three months ago. Pentagon spokesman Pat Ryder said there was "a small number [of North Korean troops] in the Kursk oblast, with a couple of thousand more that are almost there or due to arrive imminently". A senior South Korean official told reporters on Wednesday that about 3,000 North Korean troops were being moved close to the front lines. NATO Secretary-General Mark Rutte confirmed the deployment on Monday.

Ukraine continues to reel from Russia's missile strike on Monday, which ranks as the largest attack since the start of the war, as Moscow is beginning to suggest that Ukraine could make desperate moves. "Russia's large-scale strikes on Ukraine's critical infrastructure on Monday are almost certainly in response to Ukraine's incursion into Kursk Oblast, breaching Russia's border," Rebekah Koffler, told Fox News Digital. "Zelenskyy likely anticipated Russia's retaliation and accepted the risk anyway," Koffler explained. "Zelenskyy wants to stay in the fight - there's no other path for him personally or professionally." "To stay in the fight, he needs more weapons and financing from the West," she added.

With the expansion of social media and the increasing dissemination of multimedia content, the spread of misinformation has become a major concern. This necessitates effective strategies for multimodal misinformation detection (MMD) that detect whether the combination of an image and its accompanying text could mislead or misinform. Due to the data-intensive nature of deep neural networks and the labor-intensive process of manual annotation, researchers have been exploring various methods for automatically generating synthetic multimodal misinformation - which we refer to as Synthetic Misinformers - in order to train MMD models. However, limited evaluation on real-world misinformation and a lack of comparisons with other Synthetic Misinformers makes difficult to assess progress in the field. To address this, we perform a comparative study on existing and new Synthetic Misinformers that involves (1) out-of-context (OOC) image-caption pairs, (2) cross-modal named entity inconsistency (NEI) as well as (3) hybrid approaches and we evaluate them against real-world misinformation; using the COSMOS benchmark. The comparative study showed that our proposed CLIP-based Named Entity Swapping can lead to MMD models that surpass other OOC and NEI Misinformers in terms of multimodal accuracy and that hybrid approaches can lead to even higher detection accuracy. Nevertheless, after alleviating information leakage from the COSMOS evaluation protocol, low Sensitivity scores indicate that the task is significantly more challenging than previous studies suggested. Finally, our findings showed that NEI-based Synthetic Misinformers tend to suffer from a unimodal bias, where text-only MMDs can outperform multimodal ones.