This image shows a research poster presented at the Computer Vision and Pattern Recognition (CVPR) conference. The poster is authored by Matteo Farina, Massimiliano Mancini, Eila Cuognetti, Gaowen Liu, Giovanni Iacobacci, and Elisa Ricci from the University of Trento and FBK. The research is titled "MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning."

Key elements of the poster include:
1. **Introduction to VLM Pruning Problems**: Highlighting issues of speed, practicality, and the need for frequent retraining in traditional task-specific Vision-Language Model (VLM) pruning.
2. **Universal VLM Pruning Approach**: Proposing a task-agnostic pruning methodology to improve efficiency and adaptability across various tasks without needing to prune for each separately.
3. **Multimodal Flow Pruning**: Introducing strategies like Information Flow and Multimodality-aware Compression to enhance pruning methodologies.
4. **Experimental Results**: Providing detailed experimental data on image-text retrieval, image captioning, and visual question answering (VQA) performance at various sparsity levels.
5. **Collaborative and Funding Acknowledgements**: Featuring logos of supporting institutions and collaborators, including Cisco, Università di Trento, and Fondazione Bruno Kessler. 

The poster also visually represents the workflow of both traditional and proposed pruning methods and substantial quantitative comparisons to underline the benefits of the new task-agnostic approach.
Text transcribed from the image:
CISCO
ERSITAS
ATHESIA
UNIVERSITÀ
DI TRENTO
Ex
FONDAZIONE
BRUNO KESSLER
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language Pruning
Matteo Farina1, Massimiliano Mancini, Elia Cunegatti, Gaowen Liu, Giovanni lacca, Elisa Ricci1.3
(1) University of Trento, (2) CISCO Research, Fondazione Bruno Kessler
Multimodal Flow Pruning
Some problems in VLM Pruning
Speed: Prior works focus on gradually pruning during
training. Practicality: one must re-prune whenever the
downstream task changes.
Can we find a universal
pruned VLM?
Task-Specific VLM Pruning (current)
t₁
CAPTIONING
"a brown tower
with a clock on top."
Pruned
VLM
Dense VLM
"A plate of food and
a glass of liquid."
t2
RETRIEVAL
"a cat lying down
on a bicycle seat."
Pruned
VLM
"How many street
lights do you see?"
"One."
t|T|
VQA
Pruned
VLM
Dense
VLM
Dg
801-2
Information Flow
S(A)-S)--S(r)
Multimodality-aware Compression
O
10€ top (1-e))
O
CVPR
SEATTLE, WANEL
151
Sparsity across modalities
TH
Global Saliency Score
Beyond layer collage modality collapsal
Activations and weight magnitudes vary significantly across layers
and modalities. Global pruning may wipe out a whole modality
Pruning runtime
Pruned VLM
6
Information Flow Parameter importance includes the parameter itself and the neurons it connects.
Multimodality-aware Compression Equal global sparsities allocated for different modality groups.
Experimental Results at moderate sparsities
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Mabod
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CHITA++
MULTIPLO
45
X
MES
Image-Text Retrieval
BLIP
XVLM
Method
Sparsity
Image-to-Test (9)
Text-to-Image (9)
ImageText
Text -an-Image (5)
Mad
VOA
Caplining
Task-Agnostic Vision-Language Pruning (ours)
ROI
ROS
ROI
ROS
201
ROS
Re
ROS
U
cob
Image Captioning & VQA
6
Extreme sparsity
AULAN
t₁
CAPTIONING
DENSE
RANDOM
SNIP
0%
80.72
62.99
8527
78.18
944
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0.02
0.10
21.48
40.00
14.3
37.1
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68.06
963
31.35
78.61
2019
91.27
$141
022
INP
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3.M
13AN
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7539
92.95
1232
5871
26.03
115
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1.26
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202
11.M
114.25
#
1
63%
t2
Pruned
VLM
...RETRIEVAL
LAMP
CHITA++
MULTIFLOW
RANDOM
SNIP
70.36
ALD
NA
75.32 Ma
81.27
38.36
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12.29
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91.20
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154
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7631
9327
KLAR
77.35
BLS
6421
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75%
A
85.97
48.28
25.47
30.27
9091
BUST
NO
Dense VLM
OMP
75%
"a group of buildings
tITI
VQA
210
5.93
435
9097
26
SO
CATA
1105
NOAT
LAMP
7625
MELDALON
ELS CINECA FRE
6462
$7.07
under nice blue sky."
CHITA
MULTIPLOW
65.73 $7.97 4.5
77.38
4