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Found 13 papers, 6 papers with code
Rethinking Denoised Auto-Encoding in Language Pre-Training
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
TempCompass: Do Video LLMs Really Understand Videos?
Motivated by these two problems, we propose the \textbf{TempCompass} benchmark, which introduces a diversity of temporal aspects and task formats.
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding.
VITATECS: A Diagnostic Dataset for Temporal Concept Understanding of Video-Language Models
The ability to perceive how objects change over time is a crucial ingredient in human intelligence.
RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge
Therefore, to evaluate the ability of LLMs to discern the reliability of external knowledge, we create a benchmark from existing knowledge bases.
FETV: A Benchmark for Fine-Grained Evaluation of Open-Domain Text-to-Video Generation
The multi-aspect categorization of FETV enables fine-grained analysis of the metrics' reliability in different scenarios.
TESTA: Temporal-Spatial Token Aggregation for Long-form Video-Language Understanding
TESTA can reduce the number of visual tokens by 75% and thus accelerate video encoding.
Ranked #1 on
Video Retrieval
on Condensed Movies
(using extra training data)
An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures
In complex networks, interactions between multiple agents give rise to an array of intricate global dynamics, ranging from synchronization to cluster formations.
M$^3$IT: A Large-Scale Dataset towards Multi-Modal Multilingual Instruction Tuning
To tackle this challenge and promote research in the vision-language field, we introduce the Multi-Modal, Multilingual Instruction Tuning (M$^3$IT) dataset, designed to optimize VLM alignment with human instructions.
Optimizing Energy Efficiency in Metro Systems Under Uncertainty Disturbances Using Reinforcement Learning
In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit.
CAPT: Contrastive Pre-Training for Learning Denoised Sequence Representations
Pre-trained self-supervised models such as BERT have achieved striking success in learning sequence representations, especially for natural language processing.
DCA: Diversified Co-Attention towards Informative Live Video Commenting
In this paper, we aim to collect diversified information from video and text for informative comment generation.
