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Found 10 papers, 5 papers with code
UNIAA: A Unified Multi-modal Image Aesthetic Assessment Baseline and Benchmark
To further evaluate the IAA capability of MLLMs, we construct the UNIAA-Bench, which consists of three aesthetic levels: Perception, Description, and Assessment.
MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators
Recent advances in Text-to-Video generation (T2V) have achieved remarkable success in synthesizing high-quality general videos from textual descriptions.
LLMBind: A Unified Modality-Task Integration Framework
In the multi-modal domain, the dependence of various models on specific input formats leads to user confusion and hinders progress.
MoE-LLaVA: Mixture of Experts for Large Vision-Language Models
In this work, we propose a simple yet effective training strategy MoE-Tuning for LVLMs.
Ranked #57 on
Visual Question Answering
on MM-Vet
Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models
Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user inquiries.
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
In this work, we unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM.
Ranked #3 on
Zero-Shot Video Question Answer
on TGIF-QA
Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions
Initially, we utilize DeepONet to learn the solution operator for a set of smooth problems relevant to the PDEs characterized by sharp solutions.
LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment
We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M.
Ranked #1 on
Zero-shot Audio Classification
on VGG-Sound
(using extra training data)
Automated Identification of On-hold Self-admitted Technical Debt
Self-admitted technical debt (SATD) is a particular form of technical debt: developers consciously perform the hack but also document it in the code by adding comments as a reminder (or as an admission of guilt).
Software Engineering
Toward an Automated Auction Framework for Wireless Federated Learning Services Market
In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning.
Computer Science and Game Theory
