Video data is growing exponentially daily due to the popularity of video-sharing platforms and the proliferation of video capture devices. The video summarization task has been proposed to remove redundancy while maintaining as many critical parts of the video as possible so that users can browse and process videos more effectively, which has received increasing attention from researchers. The existing research addresses the challenges faced by video summarization methods from various perspectives, such as temporal dependency, data scarcity, user preference, and high precision. This paper reviews representative and state-of-the-art methods, analyzes recent research advances, datasets, and performance evaluations, and discusses future directions. We hope this survey can help future research explore the potential directions of video summarization methods.

Video summarization aims to eliminate visual redundancy while retaining key parts of video to construct concise and comprehensive synopses. Most existing methods use discriminative models to predict the importance scores of video frames. However, these methods are susceptible to annotation inconsistency caused by the inherent subjectivity of different annotators when annotating the same video. In this paper, we introduce a generative framework for video summarization that learns how to generate summaries from a probability distribution perspective, effectively reducing the interference of subjective annotation noise. Specifically, we propose a novel diffusion summarization method based on the Denoising Diffusion Probabilistic Model (DDPM), which learns the probability distribution of training data through noise prediction, and generates summaries by iterative denoising. Our method is more resistant to subjective annotation noise, and is less prone to overfitting the training data than discriminative methods, with strong generalization ability. Moreover, to facilitate training DDPM with limited data, we employ an unsupervised video summarization model to implement the earlier denoising process. Extensive experiments on various datasets (TVSum, SumMe, and FPVSum) demonstrate the effectiveness of our method.

Estimating the difficulty of input questions as perceived by large language models (LLMs) is essential for accurate performance evaluation and adaptive inference.Existing methods typically rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself, which may incur substantial computational costs or compromise generality.In this paper, we propose a novel approach for difficulty estimation that leverages only the hidden representations produced by the target LLM.We model the token-level generation process as a Markov chain and define a value function to estimate the expected output quality given any hidden state.This allows for efficient and accurate difficulty estimation based solely on the initial hidden state, without generating any output tokens.Extensive experiments across both textual and multimodal tasks demonstrate that our method consistently outperforms existing baselines in difficulty estimation.Moreover, we apply our difficulty estimates to guide adaptive reasoning strategies, including Self-Consistency, Best-of-N, and Self-Refine, achieving higher inference efficiency with fewer generated tokens.

Text-to-image synthesis has become highly popular for generating realistic and stylized images, often requiring fine-tuning generative models with domain-specific datasets for specialized tasks. However, these valuable datasets face risks of unauthorized usage and unapproved sharing, compromising the rights of the owners. We address the issue of dataset abuse during the fine-tuning of Stable Diffusion (SD) models for text-to-image (T2I) synthesis. We present a dataset watermarking framework designed to detect unauthorized usage and trace data leaks. Experiments demonstrate the framework's effectiveness, minimal impact on the dataset (only 2% of the data required to be modified for high detection accuracy), and ability to trace data leaks. Our results also highlight the transferability and robustness of the framework, proving its practical applicability in detecting dataset abuse. The code is available: https://github.com/inmbzpmdqv/treidzxyq.git