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Found 21 papers, 11 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.
MiMo-VL Technical Report
We open-source MiMo-VL-7B-SFT and MiMo-VL-7B-RL, two powerful vision-language models delivering state-of-the-art performance in both general visual understanding and multimodal reasoning.
MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining
We present MiMo-7B, a large language model born for reasoning tasks, with optimization across both pre-training and post-training stages.
TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos
Remarkably, our experiments demonstrate that DTD achieves an 82. 8% reduction in video tokens while maintaining 98% performance on StreamingBench, revealing that over 80% of visual content in streaming videos is naturally redundant without requiring language guidance.
QAMA: Quantum annealing multi-head attention operator with classical deep learning framework
As large language models scale up, the conventional attention mechanism faces critical challenges of exponential growth in memory consumption and energy costs.
TEMPLE:Temporal Preference Learning of Video LLMs via Difficulty Scheduling and Pre-SFT Alignment
We further analyze the transferability of DPO data across architectures and the role of difficulty scheduling in optimization.
Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs
Ultimately, our experimental findings demonstrate that a 300B MoE LLM can be effectively trained on lower-performance devices while achieving comparable performance to models of a similar scale, including dense and MoE models.
PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension
To provide a comprehensive evaluation, PunchBench incorporates diverse question formats and image-captions from various domains.
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
The mathematical capabilities of Multi-modal Large Language Models (MLLMs) remain under-explored with three areas to be improved: visual encoding of math diagrams, diagram-language alignment, and chain-of-thought (CoT) reasoning.
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.
Ranked #2 on
Video-Text Retrieval
on Test-of-Time
(using extra training data)
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.
