Hugging Face Trending Papers June 23, 2026

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

Read the original on Hugging Face Trending Papers →

Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning. Since audio and visual streams naturally co-occur in video data, extending this success to jointly learn from both modalities is a natural next step, yet it remains challenging.

Summary generated by The Flow from the publisher's feed. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Jun 25

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

arXiv:2606. 25225v1 Announce Type: cross Abstract: Self-supervised learning from large-scale video data has emerged as a dominant paradigm for visual representation learning.

By Revant Teotia, Adrien Bardes, Michael Rabbat, Sumit Chopra, Matthew J. Muckley, Nicolas Ballas

rag multimodal benchmarks

arXiv AI

Jul 20

AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning

arXiv:2607. 15295v1 Announce Type: cross Abstract: We present AV-JEPA, an elegant multimodal extension of LeJEPA to audio-visual self-supervised learning.

By Benjamin Robson, Santeri Mentu, Wenshuai Zhao, Arno Solin

llms rag diffusion computer-vision multimodal safety

arXiv Machine Learning

Jun 16

MVEB: Massive Video Embedding Benchmark

arXiv:2606. 14958v1 Announce Type: cross Abstract: We introduce the Massive Video Embedding Benchmark (MVEB), a 23-task benchmark for video embeddings spanning classification, zero-shot classification, clustering, pair classification, retrieval, and video-centric question answering.

By Adnan El Assadi, Roman Solomatin, Isaac Chung, Chenghao Xiao, Deep Shah, Manan Dey, Shriya Sudhakar, Zacharie Bugaud, Wissam Siblini, Ayush Sunil Munot, Yashwanth Devavarapu, Rakshitha Ireddi, Michelle Yang, M\'arton Kardos, Niklas Muennighoff, Kenneth Enevoldsen

rag nlp multimodal benchmarks

arXiv Machine Learning

Jul 14

Empowering Long-form Omni-modal Understanding with Robust Audio Perception

arXiv:2607. 10299v1 Announce Type: new Abstract: Recent advances in large-scale multimodal models have drivenremarkable progress in vision-language tasks; however, comprehensiveomni-modal understanding remains under-explored, largely due to thescarcity of datasets with rich, explicitly aligned auditory cues.

By Kaiying Yan, Luoyi Sun, Xiao Zhou, Weidi Xie

fine-tuning multimodal benchmarks

arXiv AI

Jun 6

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

arXiv:2606. 05173v1 Announce Type: cross Abstract: Masked language modelling (MLM) has been the dominant pre-training objective for text encoders since BERT, yet it encourages representations that are strongly anchored to surface-form token identity rather than deeper semantic structure.

By Aimen Boukhari

rag diffusion benchmarks

arXiv Machine Learning

Aug 4

Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data

arXiv:2608. 01336v1 Announce Type: cross Abstract: Modern autonomous-driving fleets record far more video than human reviewers can inspect.

By Advait Pavuluri, Shamik Karkhanis, Uzma Mushtaque

rag agents fine-tuning benchmarks

Related stories

MJEPA: A Simple and Scalable Joint-Embedding Predictive Architecture for Audio-Visual Learning

AV-JEPA: Extending LeJEPA to Audio-Visual Self-Supervised Learning

MVEB: Massive Video Embedding Benchmark

Empowering Long-form Omni-modal Understanding with Robust Audio Perception

Predict and Reconstruct: Joint Objectives for Self-Supervised Language Representation Learning

Asleep at the Wheel: JEPA's Limitations in Evaluating Novel Driving Data