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David Fan

7 papers hereh-index 6724 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author5

Across the 6 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.AI1
  • cs.LG1
  • cs.RO1
same name
  • David Fan — 1 paper
  • David Fan — 1 paper, h 1
  • David Fan — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedV-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

5 citations · 5 across the 3 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

Beyond Language Modeling: An Exploration of Multimodal Pretraining

Shengbang Tong, David Fan, John Nguyen +18

The visual world offers a critical axis for advancing foundation models beyond language. Despite growing interest in this direction, the design space for native multimodal models r…

cs.CV2026

A Lightweight Library for Energy-Based Joint-Embedding Predictive Architectures

Basile Terver, Randall Balestriero, Megi Dervishi +8

We present EB-JEPA, an open-source library for learning representations and world models using Joint-Embedding Predictive Architectures (JEPAs). JEPAs learn to predict in represent…

cs.CV2025

Scaling Language-Free Visual Representation Learning

David Fan, Shengbang Tong, Jiachen Zhu +8

Visual Self-Supervised Learning (SSL) currently underperforms Contrastive Language-Image Pretraining (CLIP) in multimodal settings such as Visual Question Answering (VQA). This mul…

cs.CV2024

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

Shengbang Tong, David Fan, Jiachen Zhu +7

In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.