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Found 12 papers, 4 papers with code
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding
The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged.
Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications.
AVID: Any-Length Video Inpainting with Diffusion Model
Given a video, a masked region at its initial frame, and an editing prompt, it requires a model to do infilling at each frame following the editing guidance while keeping the out-of-mask region intact.
Aggregate Modeling and Equilibrium Analysis of the Crowdsourcing Market for Autonomous Vehicles
We call such a mobility market with AV renting options the "AV crowdsourcing market".
Analyzing the Performance of Graph Neural Networks with Pipe Parallelism
Many interesting datasets ubiquitous in machine learning and deep learning can be described via graphs.
PPL Bench: Evaluation Framework For Probabilistic Programming Languages
The benchmark includes data generation and evaluation code for a number of models as well as implementations in some common PPLs.
Distinguish Confusing Law Articles for Legal Judgment Prediction
Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public.
Learning to Order Sub-questions for Complex Question Answering
Answering complex questions involving multiple entities and relations is a challenging task.
Consistent Classification with Generalized Metrics
We propose a framework for constructing and analyzing multiclass and multioutput classification metrics, i. e., involving multiple, possibly correlated multiclass labels.
Answering Science Exam Questions Using Query Reformulation with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
Answering Science Exam Questions Using Query Rewriting with Background Knowledge
Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
Improving Natural Language Inference Using External Knowledge in the Science Questions Domain
To address this, we present a combination of techniques that harness knowledge graphs to improve performance on the NLI problem in the science questions domain.
