LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment (2310.01852v7)

Abstract: The video-language (VL) pretraining has achieved remarkable improvement in multiple downstream tasks. However, the current VL pretraining framework is hard to extend to multiple modalities (N modalities, N>=3) beyond vision and language. We thus propose LanguageBind, taking the language as the bind across different modalities because the language modality is well-explored and contains rich semantics. Specifically, we freeze the language encoder acquired by VL pretraining, then train encoders for other modalities with contrastive learning. As a result, all modalities are mapped to a shared feature space, implementing multi-modal semantic alignment. While LanguageBind ensures that we can extend VL modalities to N modalities, we also need a high-quality dataset with alignment data pairs centered on language. We thus propose VIDAL-10M with Video, Infrared, Depth, Audio and their corresponding Language, naming as VIDAL-10M. In our VIDAL-10M, all videos are from short video platforms with complete semantics rather than truncated segments from long videos, and all the video, depth, infrared, and audio modalities are aligned to their textual descriptions. LanguageBind has achieved superior performance on a wide range of 15 benchmarks covering video, audio, depth, and infrared. Moreover, multiple experiments have provided evidence for the effectiveness of LanguageBind in achieving indirect alignment and complementarity among diverse modalities. Code address: https://github.com/PKU-YuanGroup/LanguageBind

• The paper introduces LanguageBind, a framework that extends video-language pretraining to multiple modalities via language-based semantic alignment.
• It employs a minimal LaTeX template conforming to ICLR 2024 guidelines to standardize academic submissions.
• Though the document lacks detailed empirical results, it highlights the potential of integrating linguistic semantics to enhance multimodal learning.

Overview of an Empty LaTeX Document

The provided content represents an empty LaTeX document shell, referencing an implicit .bib file for bibliography management using the ICLR 2024 style. There are no details, hypotheses, methodologies, results, or discussions present within this document. Consequently, the task of an insightful summary becomes an examination of the document structure in the context of academic writing rather than the content itself.

Document Structure

The file presented is a minimal LaTeX template structured for research submissions:

1. Document Class: It begins by specifying the document class as article. This is a standard choice for many academic papers, offering a simple and effective default setup for creating technical documents.
2. Bibliography Management: The document includes commands for bibliography management:
   • \nocite{*} suggests that all entries in the bibliography file should be included in the reference list, even if not cited in the text.
   • \bibliography{iclr2024_conference.bib} points to the bibliography file, indicating the potential existence of references that support the research context, though none are used here.
   • \bibliographystyle{iclr2024} selects the bibliography style, tailoring the format to meet the ICLR 2024 guidelines.

Analysis and Implications

This LaTeX template is an essential tool for researchers, particularly in the field of computer science where paper formatting standards are rigorous. The ICLR conference, one of the premier venues for machine learning research, imposes specific stylistic and formatting requirements that authors must adhere to when submitting manuscripts.

• Practical Use: The template suggests readiness for contribution to scholarly dialogues at respected conferences. It is crafted to ensure compliance with submission standards, facilitating the peer review process directly aligned with ICLR's structured presentation of academic work.
• Future Research Directions: While the template lacks content, it symbolically represents the foundational step in disseminating novel findings in machine learning. Researchers may utilize such a template for documenting experiments, formulating new theories, or presenting innovative algorithms.

Concluding Thoughts

While the absence of content in the provided document inhibits analysis of specific research contributions, the structure signifies preparation for engaging with scholarly communities. By conforming to the ICLR format, the template aids researchers in focusing on substantial content creation, allowing them to anchor their innovations within an accepted academic framework.