Search Results for author:
Found 17 papers, 6 papers with code
Linguistics-aware Masked Image Modeling for Self-supervised Scene Text Recognition
In this paper, we propose a Linguistics-aware Masked Image Modeling (LMIM) approach, which channels the linguistic information into the decoding process of MIM through a separate branch.
Cockatiel: Ensembling Synthetic and Human Preferenced Training for Detailed Video Caption
Video Detailed Captioning (VDC) is a crucial task for vision-language bridging, enabling fine-grained descriptions of complex video content.
Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
We present AlphaGeometry2, a significantly improved version of AlphaGeometry introduced in Trinh et al. (2024), which has now surpassed an average gold medalist in solving Olympiad geometry problems.
IPO: Iterative Preference Optimization for Text-to-Video Generation
Specifically, IPO exploits a critic model to justify video generations for pairwise ranking as in Direct Preference Optimization or point-wise scoring as in Kahneman-Tversky Optimization.
LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment
Based on this, we train a reward model LiFT-Critic to learn reward function effectively, which serves as a proxy for human judgment, measuring the alignment between given videos and human expectations.
VidGen-1M: A Large-Scale Dataset for Text-to-video Generation
Produced through a coarse-to-fine curation strategy, this dataset guarantees high-quality videos and detailed captions with excellent temporal consistency.
EVALALIGN: Supervised Fine-Tuning Multimodal LLMs with Human-Aligned Data for Evaluating Text-to-Image Models
Our evaluation across 24 text-to-image generation models demonstrate that EvalAlign not only provides superior metric stability but also aligns more closely with human preferences than existing metrics, confirming its effectiveness and utility in model assessment.
Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length
The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and state space models exist, they empirically underperform Transformers in pretraining efficiency and downstream task accuracy.
IPAD: Iterative, Parallel, and Diffusion-based Network for Scene Text Recognition
Nowadays, scene text recognition has attracted more and more attention due to its diverse applications.
End-to-end Story Plot Generator
Story plots, while short, carry most of the essential information of a full story that may contain tens of thousands of words.
Learning Personalized Alignment for Evaluating Open-ended Text Generation
Recent research has increasingly focused on evaluating large language models' (LLMs) alignment with diverse human values and preferences, particularly for open-ended tasks like story generation.
TorchRL: A data-driven decision-making library for PyTorch
PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments.
Masked and Permuted Implicit Context Learning for Scene Text Recognition
We utilize the training procedure of PLM, and to integrate MLM, we incorporate word length information into the decoding process and replace the undetermined characters with mask tokens.
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency.
Sample-efficient Surrogate Model for Frequency Response of Linear PDEs using Self-Attentive Complex Polynomials
Linear Partial Differential Equations (PDEs) govern the spatial-temporal dynamics of physical systems that are essential to building modern technology.
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
We introduce Nocturne, a new 2D driving simulator for investigating multi-agent coordination under partial observability.
Hybrid Composition with IdleBlock: More Efficient Networks for Image Recognition
We propose a new building block, IdleBlock, which naturally prunes connections within the block.
