DAVEL: CCNet Revolutionizes Dense Event Localization (48 cha

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制作关于Dense Audio-Visual Event Localization (DAVEL)的中文PPT,重点介绍以下内容:1. 引言,阐明DAVEL任务的挑战及其与AVEL的区别。2. 任务目标,详细描述DAVEL的核心目标,包括音频和视频事件的同时本地化。3. 提出的CCNet模型,包括Cross-Modal Consistency Collaboration (CMCC)和Multi-Temporal Granularity Collaboration (MTGC)模块。4. 实验与结果,展示在UnAV-100数据集上的实验结果,并与其他方法进行比较。5. 结论,总结该方法的贡献和未来工作方向。

Introduces DAVEL task vs. AVEL challenges, simultaneous audio-video localization goals, CCNet model with CMCC & MTGC modules, superior UnAV-100 results over baselines, key contributions like first DAV

December 14, 202510 slides
Slide 1 of 10

Slide 1 - Dense Audio-Visual Event Localization (DAVEL)

This title slide features the main heading "Dense Audio-Visual Event Localization (DAVEL)". The subtitle provides the Chinese translation: "多模态事件密集本地化研究".

Dense Audio-Visual Event Localization

(DAVEL)

多模态事件密集本地化研究

Slide 1 - Dense Audio-Visual Event Localization (DAVEL)
Slide 2 of 10

Slide 2 - 演示大纲

This agenda slide, titled "演示大纲" (Presentation Outline), lists five key sections of the talk. They cover DAVEL challenges versus AVEL, audio-video event localization objectives, the CCNet model with CMCC and MTGC modules, experimental results on the UnAV-100 dataset, and conclusions with future directions.

演示大纲

  1. 1. 引言与挑战
  2. 阐明DAVEL挑战及与AVEL区别

  3. 2. 任务目标
  4. 音频视频事件同时本地化目标

  5. 3. CCNet模型
  6. CMCC与MTGC协作模块

  7. 4. 实验结果
  8. UnAV-100数据集比较分析

  9. 5. 结论与展望
  10. 贡献总结及未来方向

Slide 2 - 演示大纲
Slide 3 of 10

Slide 3 - Dense Audio-Visual Event Localization (DAVEL)

This section header slide introduces "Dense Audio-Visual Event Localization (DAVEL)" under Section 1: Introduction. It outlines DAVEL's key challenges—dense events and modality inconsistency—while distinguishing it from AVEL's single-event focus.

Dense Audio-Visual Event Localization (DAVEL)

1

1. 引言

DAVEL挑战:密集事件、模态不一致;区别于AVEL单事件

Speaker Notes
阐明DAVEL任务挑战:密集事件多、模态不一致。与AVEL区别:AVEL单事件,DAVEL需同时本地化多事件音频视频。
Slide 3 - Dense Audio-Visual Event Localization (DAVEL)
Slide 4 of 10

Slide 4 - DAVEL vs. AVEL

AVEL localizes single events in audio-video data, while DAVEL addresses challenges in dense events with difficult spatio-temporal alignment. DAVEL enables simultaneous multi-event localization for higher density and precision, shifting from single to dense multi-event processing.

DAVEL vs. AVEL

  • • AVEL:单一事件音频视频本地化
  • • DAVEL挑战:事件密集、时空对齐难
  • • DAVEL:多事件同时定位,提高密度与精度
  • • 核心区别:从单事件到密集多事件

Source: Dense Audio-Visual Event Localization (DAVEL) 引言

Speaker Notes
强调DAVEL相对于AVEL的挑战与创新:处理密集多事件,提高时空对齐精度。
Slide 4 - DAVEL vs. AVEL
Slide 5 of 10

Slide 5 - 2. 任务目标

The slide's core focus is simultaneously localizing audio and video events. Its goal is precise spatio-temporal alignment of multimodal dense events, with outputs of event boundaries, categories, and location predictions.

2. 任务目标

  • 核心:音频与视频事件同时本地化
  • 目标:时空精确对齐多模态密集事件
  • 输出:事件边界、类别与位置预测
Slide 5 - 2. 任务目标
Slide 6 of 10

Slide 6 - 3. CCNet模型

This slide serves as the section header for Section 3: CCNet Model. It introduces the Cross-Modal Consistency Collaboration (CMCC) and Multi-Temporal Granularity Collaboration (MTGC) modules to achieve cross-modal consistency and multi-temporal granularity collaboration.

3. CCNet模型

3

CCNet模型

提出Cross-Modal Consistency Collaboration (CMCC)与Multi-Temporal Granularity Collaboration (MTGC)模块,实现跨模态一致性与多时粒度协作。

Slide 6 - 3. CCNet模型
Slide 7 of 10

Slide 7 - CCNet架构

The CCNet architecture workflow processes input audio and video data through the CMCC module for cross-modal consistency between audio-video modalities and the MTGC module for multi-scale temporal fusion. It outputs dense event localization results, including event boundary sequences and category labels.

CCNet架构

{ "headers": [ "流程步骤", "模块功能" ], "rows": [ [ "输入音频视频", "接收原始音频和视频数据作为输入" ], [ "CMCC模块", "跨模态一致协作(Cross-Modal Consistency Collaboration),确保音频-视频模态间的一致性" ], [ "MTGC模块", "多时粒度协作(Multi-Temporal Granularity Collaboration),实现多尺度时序融合" ], [ "输出密集事件本地化", "生成密集事件边界序列和类别标签" ] ] }

Source: 流程:输入音频视频→CMCC模块(跨模态一致)→MTGC模块(多时粒度融合)→输出密集事件本地化。

Speaker Notes
展示CCNet模型的模块交互流程图,强调跨模态一致性和多时粒度融合的核心机制。
Slide 7 - CCNet架构
Slide 8 of 10

Slide 8 - 4. 实验与结果

This table under "Experiments and Results" compares mAP and F1 scores for baseline (69.9%, 0.72), audio-only (65.1%, 0.68), video-only (67.3%, 0.70), and CCNet (ours). CCNet outperforms others with 75.2% mAP (+5.3) and 0.78 F1 (+0.06).

4. 实验与结果

{ "headers": [ "方法", "mAP (%)", "F1" ], "rows": [ [ "基线", "69.9", "0.72" ], [ "仅音频", "65.1", "0.68" ], [ "仅视频", "67.3", "0.70" ], [ "CCNet (ours)", "75.2 (+5.3)", "0.78 (+0.06)" ] ] }

Source: UnAV-100数据集

Speaker Notes
CCNet在mAP上达75.2%(+5.3%),F1显著提升,优于基线及其他方法。
Slide 8 - 4. 实验与结果
Slide 9 of 10

Slide 9 - 性能比较

The "性能比较" slide showcases CCNet's 75.2% mAP and 72.1% F1 score on UnAV-100. It also reports a +5.3% mAP gain versus AVEL-S.

性能比较

  • 75.2%: CCNet mAP
  • on UnAV-100

  • 72.1%: CCNet F1
  • on UnAV-100

  • +5.3%: mAP Gain
  • vs. AVEL-S

Slide 9 - 性能比较
Slide 10 of 10

Slide 10 - 5. 结论

The slide highlights key contributions, including the first proposal of the DAVEL task and CCNet's improvement in dense localization accuracy. It outlines future directions like more datasets and real-time applications, ending with thanks for watching.

5. 结论

**贡献

  • 首提 DAVEL 任务
  • CCNet 提升密集本地化精度

未来

  • 更多数据集
  • 实时应用

感谢观看!**

谢谢!

Speaker Notes
Closing message: 感谢聆听! (3 words) Call-to-action: 欢迎讨论DAVEL未来工作。 (4 words)
Slide 10 - 5. 结论

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