05-08-2022, 04:59 PM
The Ukraine crisis deepened further after Russian President Putin announced recognising two breakaway regions of Ukraine as independent states. In his address at the French Institute of International Relations, Jaishankar extensively delved into fast-expanding Indo-French ties and said India looks at France as a “trusted” partner in countering myriad security challenges from the seabed to space and from cyber to oceans. “I can assert with genuine confidence that it (Indo-French ties) is the strongest now since our journey as an independent nation began 75 years ago,” he said. “Through the tumult of our times, India’s relations with France have continued to move forward on a steady and clear course. It is a relationship that is free from sudden shifts and surprises that we sometimes see in other cases,” Jaishankar added. “French support played an important role in India getting an exemption from the Nuclear Suppliers Group in 2008 to resume international cooperation in civil nuclear energy,” he added. Asked to compare the Ukraine crisis with the situation in Taiwan, Jaishankar said different problems have different histories, different contexts and players and transposing issues of one theatre to another can be misleading. “Both are products of very complex histories of that particular region. In the case of Ukraine, a lot of it derives from the post-Soviet politics, the expansion of NATO, the dynamics between Russia and Europe, and Russia and the West broadly,” he said. Pretraining techniques for generative tasks such as ours, are fewer. In contrast, we make use of utterances outside of the clip boundary, which are simply ignored in previous works. We leverage future utterances as a second source of textual data, and propose a bi-directional generation objective where the model generates the future utterance given the current utterance and vice versa. While we also use a masked language modelling loss, this is simply in addition to our primary generative bidirectional loss. Our objective is to jointly learn a multimodal video encoder that can effectively encode both the visual frames and transcribed speech into a set of multimodal features, and a sentence decoder that generates a caption in natural language describing the video content using the encoded features. Our model consists of modality specific encoders, a multimodal encoder and a text decoder, all of which are transformer-based architectures (Figure 2). We first describe our pretraining objectives, and then the network architecture in detail. pickleball
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