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Found 8 papers, 2 papers with code
Neuron-Level Sequential Editing for Large Language Models
This work explores sequential model editing in large language models (LLMs), a critical task that involves modifying internal knowledge within LLMs continuously through multi-round editing, each incorporating updates or corrections to adjust the model outputs without the need for costly retraining.
STAR: Scale-wise Text-to-image generation via Auto-Regressive representations
We present STAR, a text-to-image model that employs scale-wise auto-regressive paradigm.
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos
On the whole, the "multi-scale" mechanism is capable of exploiting the different levels of semantic information of each modality which are used for fine-grained crossmodal interactions.
Expanding Large Pre-Trained Unimodal Models With Multimodal Information Injection for Image-Text Multimodal Classification
Through the proposed MI2P unit, we can inject the language information into the vision backbone by attending the word-wise textual features to different visual channels, as well as inject the visual information into the language backbone by attending the channel-wise visual features to different textual words.
Computation of Reachable Sets Based on Hamilton-Jacobi-Bellman Equation with Running Cost Function
A novel method for computing reachable sets is proposed in this paper.
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network
Face reenactment is a challenging task, as it is difficult to maintain accurate expression, pose and identity simultaneously.
Attention Is Not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion
To this end, both the marginal distribution and the elements with high-confidence correlations are aligned over the common space of the query and key vectors which are computed from different modalities.
Weakly-supervised Domain Adaption for Aspect Extraction via Multi-level Interaction Transfer
Specifically, the aspect category information is used to construct pivot knowledge for transfer with assumption that the interactions between sentence-level aspect category and token-level aspect terms are invariant across domains.
