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Found 10 papers, 3 papers with code
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual Grounding
Specifically, we propose \textbf{DARA}, a novel PETL method comprising \underline{\textbf{D}}omain-aware \underline{\textbf{A}}dapters (DA Adapters) and \underline{\textbf{R}}elation-aware \underline{\textbf{A}}dapters (RA Adapters) for VG.
Large Language Model based Situational Dialogues for Second Language Learning
In second language learning, scenario-based conversation practice is important for language learners to achieve fluency in speaking, but students often lack sufficient opportunities to practice their conversational skills with qualified instructors or native speakers.
MM-Diff: High-Fidelity Image Personalization via Multi-Modal Condition Integration
CLS embeddings are used on the one hand to augment the text embeddings, and on the other hand together with patch embeddings to derive a small number of detail-rich subject embeddings, both of which are efficiently integrated into the diffusion model through the well-designed multimodal cross-attention mechanism.
EffiVED:Efficient Video Editing via Text-instruction Diffusion Models
Large-scale text-to-video models have shown remarkable abilities, but their direct application in video editing remains challenging due to limited available datasets.
AnimateAnything: Fine-Grained Open Domain Image Animation with Motion Guidance
Image animation is a key task in computer vision which aims to generate dynamic visual content from static image.
How to Evaluate Your Dialogue Models: A Review of Approaches
Evaluating the quality of a dialogue system is an understudied problem.
Erroneous data generation for Grammatical Error Correction
It has been demonstrated that the utilization of a monolingual corpus in neural Grammatical Error Correction (GEC) systems can significantly improve the system performance.
How Do Drivers Allocate Their Potential Attention? Driving Fixation Prediction via Convolutional Neural Networks
Based on the multiple drivers’ attention allocation dataset, we propose a convolutional-deconvolutional neural network (CDNN) to predict the drivers’ eye fixations.
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments.
CLUF: a Neural Model for Second Language Acquisition Modeling
Second Language Acquisition Modeling is the task to predict whether a second language learner would respond correctly in future exercises based on their learning history.
