Microsoft’s Florence-2: Bridging the Gap Between LLMs and Large Vision Models

Microsoft’s
Florence-2
represents
a
significant
leap
in
the
field
of
computer
vision,
drawing
inspiration
from
the
advancements
in
large
language
models
(LLMs)
to
create
a
foundational
image
model
capable
of
performing
a
wide
range
of
tasks.
According
to

AssemblyAI,
Florence-2
can
execute
nearly
every
common
task
in
computer
vision,
marking
a
pivotal
moment
in
the
development
of
large
vision
models
(LVMs).

Florence-2’s
Capabilities

Florence-2
is
designed
to
handle
various
image-language
tasks,
producing
image-level,
region-level,
and
pixel-level
outputs.
Some
of
the
tasks
it
can
perform
out-of-the-box
include
captioning,
optical
character
recognition
(OCR),
object
detection,
region
detection,
region
segmentation,
and
vocabulary
segmentation.
This
versatility
is
achieved
without
the
need
for
architectural
modifications,
providing
a
seamless
experience
for
users.

Challenges
in
Developing
LVMs

One
of
the
primary
challenges
in
developing
LVMs
is
instilling
the
ability
to
operate
at
different
levels
of
semantic
and
spatial
resolution.
Florence-2
addresses
this
by
leveraging
a
unified
architecture
and
a
large,
diverse
dataset,
following
the
successful
playbook
of
LLM
research.
This
approach
allows
Florence-2
to
learn
general
representations
that
are
useful
for
a
variety
of
tasks,
making
it
a
foundational
model
in
the
field
of
computer
vision.

Architecture
and
Dataset

Florence-2
employs
a
classic
seq2seq
transformer
architecture,
where
both
visual
and
textual
inputs
are
mapped
into
embeddings
and
fed
into
the
transformer
encoder-decoder.
The
model
is
trained
using
the
FLD-5B
dataset,
which
contains
5.4
billion
annotations
on
126
million
images.
This
extensive
dataset
includes
text
annotations,
text-region
annotations,
and
text-phrase-region
annotations,
enabling
the
model
to
learn
across
various
levels
of
granularity.

Training
and
Performance

Florence-2’s
training
process
involves
standard
language
modeling
with
cross-entropy
loss.
The
model
uses
a
singular
network
architecture,
a
large
and
diverse
dataset,
and
a
unified
pre-training
framework
to
achieve
significant
performance
heights.
The
inclusion
of
location
tokens
in
the
tokenizer’s
vocabulary
allows
Florence-2
to
process
region-specific
information
in
a
unified
learning
format,
eliminating
the
need
for
task-specific
heads
for
different
tasks.

How
to
Use
Florence-2

Getting
started
with
Florence-2
is
straightforward,
with
resources
like
the
Florence-2
inference
Colab
and
GitHub
repository
providing
helpful
guides
and
code
snippets.
Users
can
perform
various
tasks
such
as
captioning,
OCR,
object
detection,
segmentation,
region
description,
and
phrase
grounding
by
following
the
provided
instructions.

Future
Prospects

Florence-2
is
a
significant
step
forward
in
the
development
of
LVMs,
demonstrating
strong
zero-shot
performance
and
attaining
state-of-the-art
results
on
several
tasks
once
finetuned.
However,
further
work
is
needed
to
develop
an
LVM
that
can
perform
novel
tasks
via
in-context
learning,
similar
to
LLMs.
Researchers
and
developers
are
encouraged
to
explore
Florence-2
and
contribute
to
its
ongoing
development.

For
more
information
on
the
development
of
LVMs
and
other
AI
advancements,
subscribe
to
AssemblyAI’s

newsletter
and
check
out
their
other
resources
on
AI
progress.

Image
source:
Shutterstock