Latest Research
PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning
magic-research/PLLaVA
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arXiv 2024
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
Ranked #1 on
Zero-Shot Video Question Answer
on TGIF-QA
Video-based Generative Performance Benchmarking (Consistency)
Video-based Generative Performance Benchmarking (Contextual Understanding)
+4
History repeats itself: A Baseline for Temporal Knowledge Graph Forecasting
nec-research/recurrency_baseline_tkg
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Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs.
Vision-based robot manipulation of transparent liquid containers in a laboratory setting
danischober/labliquidvision
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Laboratory processes involving small volumes of solutions and active ingredients are often performed manually due to challenges in automation, such as high initial costs, semi-structured environments and protocol variability.
TinyChart: Efficient Chart Understanding with Visual Token Merging and Program-of-Thoughts Learning
Charts are important for presenting and explaining complex data relationships.
Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains
People often answer yes-no questions without explicitly saying yes, no, or similar polar keywords.
AAPL: Adding Attributes to Prompt Learning for Vision-Language Models
Through our experiments, we have identified important issues in CoOp and CoCoOp: the context learned through traditional image augmentation is biased toward seen classes, negatively impacting generalization to unseen classes.
SEED-Bench-2-Plus: Benchmarking Multimodal Large Language Models with Text-Rich Visual Comprehension
We hope that our work can serve as a valuable addition to existing MLLM benchmarks, providing insightful observations and inspiring further research in the area of text-rich visual comprehension with MLLMs.
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
hfates/ikr-net
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Yet, there is a gap in the literature to provide a well-generalized deep learning-based solution that performs well on images with unknown and highly complex degradations.
SwarmRL: Building the Future of Smart Active Systems
swarmrl/swarmrl
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This work introduces SwarmRL, a Python package designed to study intelligent active particles.
Continual Learning of Large Language Models: A Comprehensive Survey
beyonderxx/trace
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In this survey, we provide a comprehensive overview of the current research progress on LLMs within the context of CL.
