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20172026
most citedFlamingo: a Visual Language Model for Few-Shot Learning

1.3k citations · 1.5k across the 28 of their papers we have counts for

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Showing 2024 · cs.CVShow all

6 papers · 2 filters

cs.CV2024

It's Just Another Day: Unique Video Captioning by Discriminative Prompting

Toby Perrett, Tengda Han, Dima Damen +1

Long videos contain many repeating actions, events and shots. These repetitions are frequently given identical captions, which makes it difficult to retrieve the exact desired clip…

cs.CV2024

AutoAD-Zero: A Training-Free Framework for Zero-Shot Audio Description

Junyu Xie, Tengda Han, Max Bain +4

Our objective is to generate Audio Descriptions (ADs) for both movies and TV series in a training-free manner. We use the power of off-the-shelf Visual-Language Models (VLMs) and L…

cs.CV2024★ 6 cited

CountGD: Multi-Modal Open-World Counting

Niki Amini-Naieni, Tengda Han, Andrew Zisserman

The goal of this paper is to improve the generality and accuracy of open-vocabulary object counting in images. To improve the generality, we repurpose an open-vocabulary detection…

cs.CV2024

AutoAD III: The Prequel -- Back to the Pixels

Tengda Han, Max Bain, Arsha Nagrani +3

Generating Audio Description (AD) for movies is a challenging task that requires fine-grained visual understanding and an awareness of the characters and their names. Currently, vi…

cs.CV2024

GenEARL: A Training-Free Generative Framework for Multimodal Event Argument Role Labeling

Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham +2

Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the en…

cs.CV2024

Stale Diffusion: Hyper-realistic 5D Movie Generation Using Old-school Methods

Joao F. Henriques, Dylan Campbell, Tengda Han

Two years ago, Stable Diffusion achieved super-human performance at generating images with super-human numbers of fingers. Following the steady decline of its technical novelty, we…