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Bryan A. Plummer

61 papers hereh-index 255k citations87 works total

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

author position
  • first author7
  • middle author24
  • last author29

Across the 60 of 61 papers where every author was matched, so the position is known.

fields
  • cs.CV46
  • cs.CL7
  • cs.LG6
  • cs.AI2
same name
  • Bryan A. Plummer — 9 papers, h 5
  • Bryan A. Plummer — 6 papers, h 3
  • Bryan A. Plummer — 4 papers, h 3
  • Bryan A. Plummer — 2 papers
  • Bryan A. Plummer — 1 paper, h 3

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
20152026
most citedDetecting Cross-Modal Inconsistency to Defend Against Neural Fake News

56 citations · 257 across the 54 of their papers we have counts for

collaborators
Showing 2025 · cs.CVShow all

4 papers · 2 filters

cs.CV2025

Mull-Tokens: Modality-Agnostic Latent Thinking

Arijit Ray, Ahmed Abdelkader, Chengzhi Mao +5

Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey. Existing multimodal models exploring…

cs.CV2025

BabyVLM-V2: Toward Developmentally Grounded Pretraining and Benchmarking of Vision Foundation Models

Shengao Wang, Wenqi Wang, Zecheng Wang +20

Early children's developmental trajectories set up a natural goal for sample-efficient pretraining of vision foundation models. We introduce BabyVLM-V2, a developmentally grounded…

cs.CV2025

Seeing Isn't Orienting: A Cognitively Informed Hierarchical Benchmark for Object Orientation in MLLMs

Nazia Tasnim, Keanu Nichols, Yuting Yan +4

Humans develop object orientation understanding progressively, from recognizing which way an object faces to reasoning about orientations across multiple objects. Yet existing visi…

cs.CV2025

Web Artifact Attacks Disrupt Vision Language Models

Maan Qraitem, Piotr Teterwak, Kate Saenko +1

Vision-language models (VLMs) (e.g. CLIP, LLaVA) are trained on large-scale, lightly curated web datasets, leading them to learn unintended correlations between semantic concepts a…

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