Handling static images that lack inherent temporal dynamics remains a fundamental challenge for spiking neural networks (SNNs). In directly trained SNNs, static...
Reasoning over dynamic visual content remains a central challenge for multimodal large language models. Recent thinking models generate explicit reasoning trace...
Recent multimodal large language models (MLLMs) have advanced video understanding, yet most still 'think about videos' ie once a video is encoded, reasoning unf...
Recently, multi-person video generation has started to gain prominence. While a few preliminary works have explored audio-driven multi-person talking video gene...
Large Vision Language Models (VLMs) effectively bridge the modality gap through extensive pretraining, acquiring sophisticated visual representations aligned wi...
Deep learning approaches to object detection have achieved reliable detection of specific object classes in images. However, extending a model's detection capab...
Inverse heat problems refer to the estimation of material thermophysical properties given observed or known heat diffusion behaviour. Inverse heat problems have...
Recent advances in generative world models have enabled remarkable progress in creating open-ended game environments, evolving from static scene synthesis towar...
Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual ...
Underwater object tracking is challenging due to wavelength dependent attenuation and scattering, which severely distort appearance across depths and water cond...
Unifying multimodal understanding, generation and reconstruction representation in a single tokenizer remains a key challenge in building unified models. Previo...
Modern large language models become multimodal, analyzing various data formats like text and images. While fine-tuning is effective for adapting these multimoda...
