REVIEW 8 cited by
ImageBind: One Embedding Space To Bind Them All
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We present ImageBind, an approach to learn a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. We show that all combinations of paired data are not necessary to train such a joint embedding, and only image-paired data is sufficient to bind the modalities together. ImageBind can leverage recent large scale vision-language models, and extends their zero-shot capabilities to new modalities just by using their natural pairing with images. It enables novel emergent applications 'out-of-the-box' including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation. The emergent capabilities improve with the strength of the image encoder and we set a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Finally, we show strong few-shot recognition results outperforming prior work, and that ImageBind serves as a new way to evaluate vision models for visual and non-visual tasks.
Forward citations
Cited by 8 Pith papers
-
Fusion Embedding: A Unified Embedding Space for Text, Image, Video, and Audio
Trained connectors and audio-only gated adapters integrate audio into a frozen vision-language embedding space, preserving base outputs bit-exactly and yielding emergent audio-image retrieval.
-
OmniVAE: An Audio-Video VAE with Cross-Modal Alignment for Joint Generation
Jointly training audio and video VAEs with segment contrastive loss and semantic distillation yields more learnable, cross-aligned latents that improve downstream joint generation quality and sync.
-
Assessing Factual Music Comprehension in Large Audio Language Models
Standard NLP metrics fail to capture factual music understanding in audio-language models; a CLAP-based metric and an LLM-parsed factual QA protocol measure it more directly.
-
Testing chatbots on the creation of encoders for audio conditioned image generation
All chatbot-designed audio encoders failed to align with CLIP text embeddings and produced incoherent images, while showing a surprising architectural similarity across chatbots.
-
Learning to Plan via Supervised Contrastive Learning and Strategic Interpolation: A Chess Case Study
A transformer encoder trained with supervised contrastive learning on Stockfish win probabilities, combined with an advantage-axis cosine score and 6-ply beam search, reaches an estimated Elo of 2593.
-
AlphaWiSE: Adaptive Weight Interpolation for Continual Multimodal Representation Learning
Fitting one interpolation coefficient per parameter tensor on a small exemplar memory improves continual audio–image–text retrieval over individual continual-learning checkpoints.
-
Effectively obtaining acoustic, visual and textual data from videos
A video-processing pipeline created a 2.24 million-sample audio-image-text dataset, with text captions generated by BLIP from video frames.
-
Learning from Limited and Imperfect Data
A doctoral thesis compiling nine peer-reviewed papers on long-tailed image generation, long-tailed recognition, semi-supervised learning, and domain adaptation.
Discussion (0). Continue with ORCID to comment.