From Natural Language Query to Bounding Boxes
Bhavishya Pandit
Introduction
Can LVLM "Detect Objects" Like We Do?
LVLMs are now automating object detection, eliminating labeled
datasets and manual annotation, but how? Keep reading ;)
What you’ll learn:
How LVLMs interpret natural language
prompts to find objects
The hybrid workflow that combines
language models with traditional
computer vision
Challenges and practical solutions for
implementation
Real-world applications across
industries
Bhavishya Pandit
1. Conventional Object Detection
Source: Dataflair
Traditional approach mainly involves:
Tedious Data Annotation: Manually drawing boxes around
objects, one by one.
Lengthy Model Training: Training models for weeks on
annotated data.
It includes supervised models like YOLO (You Only Look
Once), Faster R-CNN, and SSD (Single Shot Detector)
The challenges with this approach:
Slow & Expensive: Data collection and annotation are HUGE
bottlenecks.
Limited Generalization: Models only worked well on what
they are trained on but fail to detect unseen objects.
.
Inflexible: Adding new objects meant starting from scratch.
Bhavishya Pandit
2. What are LVLMs?
Source: ResearchGate
LVLMs are AI systems designed to understand and connect visual
content (images) with text. They can analyze an image and generate
meaningful descriptions or answer questions about it by combining
visual recognition and language understanding.
Example: Given the image of a flamingo, an LVLM could process the
image and respond to the prompt "What is it in the image?" with:
"This is a flamingo." The model identifies the bird visually and
generates a text response based on its training
Bhavishya Pandit
3. How LVLMs Work?
Source: Datacamp
LVLMs integrate visual and textual data to generate context-aware
outputs. They function through:
Multimodal Training: Learning from image-text datasets to link
visual elements with descriptions.
Transformer Architecture: Using self-attention to align visual
features with text tokens.
Tokenization & Embedding: Mapping text tokens and visual
embeddings into a shared space.
Fine-Tuning: Adapting for tasks like image captioning, visual
question answering, and object localization.
Bhavishya Pandit
4. How LVLMs achieve Pixel-Precise
Localization?
Workflow for LVLM Object Detection & Localization:
User Prompt
Processing Stage
User
Find the red apple
Prompt Attention
mapping
Image: Vision Encoder Language Encoder
Step 1 Step 2
Code Generation
Code is created to
execute the task
Step 3
1. User Prompt: User provides a text prompt (e.g., "Find the red
apple") and an image.
2. Processing Stage:
a. Vision Encoder: Extracts visual features from the image.
b. Language Encoder: Understands the text prompt.
c. Attention Mapping: Aligns text with relevant regions in the
image.
3. Code Generation: The model generates executable code to
process the task, including steps for object localization.
Bhavishya Pandit
Descriptions into pixel
coordinates Integration Stage
Step 4 Step 5
Execution Object Detected
Generated code is run Object is identified
in an environment
precisely
Step 6 Step 7
4.Descriptions into Pixel Coordinates: Tools like OpenCV, NumPy,
and scikit-learn convert attention maps into precise pixel
coordinates for bounding boxes.
5.Integration Stage: Combines visual and textual data to refine object
localization.
6.Execution: The generated code is executed in an environment to
identify the object.
7.Object Detection: The object is localized with bounding boxes and
displayed precisely.
Bhavishya Pandit
Step 1 Step 2
User Prompt
User Processing Stage
Find the red apple
Prompt
Attention
mapping
Image:
Vision Encoder Language Encoder
Step 4
Step 3
Descriptions into pixel Code Generation
coordinates
Code is created to
execute the task
Step 5 Step 6
Integration Stage Execution
Generated code is run
in an environment
Step 7
Object Detected
Object is identified
precisely
Bhavishya Pandit
5. Tradeoffs in Object Detection
Conventional Pipeline LVLM Approach
Conventional LVLM
Aspects
Pipeline Approach
Data Needs extensive
data collection & Utilizes prompts and
Handling annotation reducing data needs
Slow due to LVLM
Time-efficient &
Inference used in real-time
latency during code
generation
Human High demand for Minimal human
human annotators intervention
Resources required
Highly adaptable to
Rigid structure,
Flexibility new asks via
difficult to adapt
prompts
Highly optimized for Depends on prompt
Performance well-defined tasks quality and domain
with enough data coverage
Bhavishya Pandit
6. Applications Across Industries
Retail
Inventory management systems that identify
products from natural language descriptions (40%
faster stocktaking).
Healthcare
Medical imaging tools that locate anomalies based on
radiologist descriptions (30% improvement in
screening efficiency).
Manufacturing
Quality control systems that detect defects from
verbal specifications without reprogramming
Autonomous
Vehicles
CLIP and Grounding DINO models enable
identification of unexpected road obstacles from
simple descriptions.
Bhavishya Pandit
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