VidTok: A Versatile and Open-Source Video Tokenizer

• URL: http://arxiv.org/abs/2412.13061v1
• Date: Tue, 17 Dec 2024 16:27:11 GMT
• Title: VidTok: A Versatile and Open-Source Video Tokenizer
• Authors: Anni Tang, Tianyu He, Junliang Guo, Xinle Cheng, Li Song, Jiang Bian,
• Abstract summary: VidTok is a versatile video tokenizer that delivers state-of-the-art performance in both continuous and discrete tokenizations. By integrating these advancements, VidTok achieves substantial improvements over existing methods.
• Score: 24.018360305535307
• License:
• Abstract: Encoding video content into compact latent tokens has become a fundamental step in video generation and understanding, driven by the need to address the inherent redundancy in pixel-level representations. Consequently, there is a growing demand for high-performance, open-source video tokenizers as video-centric research gains prominence. We introduce VidTok, a versatile video tokenizer that delivers state-of-the-art performance in both continuous and discrete tokenizations. VidTok incorporates several key advancements over existing approaches: 1) model architecture such as convolutional layers and up/downsampling modules; 2) to address the training instability and codebook collapse commonly associated with conventional Vector Quantization (VQ), we integrate Finite Scalar Quantization (FSQ) into discrete video tokenization; 3) improved training strategies, including a two-stage training process and the use of reduced frame rates. By integrating these advancements, VidTok achieves substantial improvements over existing methods, demonstrating superior performance across multiple metrics, including PSNR, SSIM, LPIPS, and FVD, under standardized evaluation settings.

Related papers

• Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models [89.79067761383855]
  Vchitect-2.0 is a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation. By introducing a novel Multimodal Diffusion Block, our approach achieves consistent alignment between text descriptions and generated video frames. To overcome memory and computational bottlenecks, we propose a Memory-efficient Training framework.
  arXiv  Detail & Related papers  (2025-01-14T21:53:11Z)
• VideoEspresso: A Large-Scale Chain-of-Thought Dataset for Fine-Grained Video Reasoning via Core Frame Selection [61.54044967253421]
  We introduce VideoEspresso, a novel dataset that features VideoQA pairs preserving essential spatial details and temporal coherence. Our construction pipeline employs a semantic-aware method to reduce redundancy, followed by generating QA pairs using GPT-4o. We propose a Hybrid LVLMs Collaboration framework, featuring a Frame Selector and a two-stage instruction fine-tuned reasoning LVLM.
  arXiv  Detail & Related papers  (2024-11-22T08:33:36Z)
• Beyond Training: Dynamic Token Merging for Zero-Shot Video Understanding [11.211803499867639]
  We propose DYTO, a novel dynamic token merging framework for zero-shot video understanding. DYTO integrates a hierarchical frame selection and a bipartite token merging strategy to dynamically cluster key frames and selectively compress token sequences. Experiments demonstrate the effectiveness of DYTO, achieving superior performance compared to both fine-tuned and training-free methods.
  arXiv  Detail & Related papers  (2024-11-21T18:30:11Z)
• When Video Coding Meets Multimodal Large Language Models: A Unified Paradigm for Video Coding [118.72266141321647]
  Cross-Modality Video Coding (CMVC) is a pioneering approach to explore multimodality representation and video generative models in video coding. During decoding, previously encoded components and video generation models are leveraged to create multiple encoding-decoding modes. Experiments indicate that TT2V achieves effective semantic reconstruction, while IT2V exhibits competitive perceptual consistency.
  arXiv  Detail & Related papers  (2024-08-15T11:36:18Z)
• Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization [52.63845811751936]
  Video pre-training is challenging due to the modeling of its dynamics video. In this paper, we address such limitations in video pre-training with an efficient video decomposition. Our framework is both capable of comprehending and generating image and video content, as demonstrated by its performance across 13 multimodal benchmarks.
  arXiv  Detail & Related papers  (2024-02-05T16:30:49Z)
• Hybrid Contrastive Quantization for Efficient Cross-View Video Retrieval [55.088635195893325]
  We propose the first quantized representation learning method for cross-view video retrieval, namely Hybrid Contrastive Quantization (HCQ) HCQ learns both coarse-grained and fine-grained quantizations with transformers, which provide complementary understandings for texts and videos. Experiments on three Web video benchmark datasets demonstrate that HCQ achieves competitive performance with state-of-the-art non-compressed retrieval methods.
  arXiv  Detail & Related papers  (2022-02-07T18:04:10Z)
• DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization [127.16984421969529]
  We introduce a novel Query-Aware Hierarchical Pointer Network for Multi-Video Summarization, termed DeepQAMVS. DeepQAMVS is trained with reinforcement learning, incorporating rewards that capture representativeness, diversity, query-adaptability and temporal coherence. We achieve state-of-the-art results on the MVS1K dataset, with inference time scaling linearly with the number of input video frames.
  arXiv  Detail & Related papers  (2021-05-13T17:33:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.