In order to thrive in hostile and ever-changing natural environments, mammalian brains evolved to store large amounts of knowledge about the world and continually integrate new information while avoiding catastrophic forgetting.
In this paper, we propose MotionFollower, a lightweight score-guided diffusion model for video motion editing.
Diffusion models have demonstrated great success in text-to-video (T2V) generation.
Simultaneous speech-to-speech translation (Simul-S2ST, a. k. a streaming speech translation) outputs target speech while receiving streaming speech inputs, which is critical for real-time communication.
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To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans.
We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods.
To address the challenges, we present LLaVA-UHD, a large multimodal model that can efficiently perceive images in any aspect ratio and high resolution.
End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities.
Our experiments demonstrate that OSEDiff achieves comparable or even better Real-ISR results, in terms of both objective metrics and subjective evaluations, than previous diffusion model based Real-ISR methods that require dozens or hundreds of steps.
We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources.