Silvr Language Video Reasoning

SiLVR: Make Videos Speak for Reasoning

Video understanding requires reasoning over temporal sequences, causal relationships, and dynamic scenes. SiLVR sidesteps video-specific model training by converting raw video into rich language representations using visual captions, audio, and speech subtitles, then feeding these descriptions to a general-purpose reasoning LLM. This two-stage decomposition—encode video as text, then reason with text—achieves top results on video QA and understanding benchmarks without ever training on video directly.

The core principle is that video understanding bottlenecks on reasoning capacity, not visual encoding. By converting video to text that preserves temporal, causal, and semantic information, we can leverage the superior reasoning capabilities of language models trained on massive text corpora. This also makes the system transparent: users can see exactly what information the model is reasoning over.

Core Concept

Most video understanding systems treat video as an input modality like language or images, building multimodal models trained end-to-end. SiLVR inverts this: treat video as a source of rich language descriptions (captions, subtitles, audio transcriptions), then apply standard LLM reasoning without any video-specific architecture. The key innovation is adaptive context reduction—dynamically determining how many frames to caption and how granular temporal sampling should be based on the query, avoiding the common problem of excessive context length.

Architecture Overview

• Video-to-Text Encoder: Extracts visual captions, speech transcripts, and audio descriptions from video frames at adaptive temporal granularity
• Context Reduction Module: Dynamically samples frames based on query complexity; fewer frames for simple questions, more for temporal reasoning tasks
• Text Integration Pipeline: Combines visual captions, subtitles, and audio into a unified narrative preserving temporal ordering and scene information
• LLM Reasoner: Off-the-shelf language model that performs temporal, causal, and knowledge-based reasoning over the text representation
• Query-Specific Sampling: Different question types receive different temporal resolution (e.g., "what colors?" → sparse sampling, "describe sequence of events?" → dense sampling)

Implementation

This implementation demonstrates the video-to-text pipeline and adaptive context reduction for multimodal reasoning.

First, build the video-to-text conversion pipeline using captions, subtitles, and audio:

import cv2
import numpy as np
from typing import List, Tuple, Dict
from dataclasses import dataclass

@dataclass
class VideoFrame:
    frame_number: int
    timestamp: float
    caption: str
    audio_description: str
    subtitle: str

class VideoToTextConverter:
    """Convert raw video frames to language-based representations."""

    def __init__(self, caption_model, transcription_model):
        self.caption_model = caption_model  # Vision captioning model
        self.transcription_model = transcription_model  # Audio/speech recognition

    def extract_video_metadata(self, video_path: str, fps: int = 2):
        """
        Extract frames at specified FPS and generate captions + audio.
        Lower FPS reduces compute; adjust based on scene complexity.
        """
        cap = cv2.VideoCapture(video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        video_fps = cap.get(cv2.CAP_PROP_FPS)
        frame_interval = int(video_fps / fps)

        frames_data = []
        frame_idx = 0

        while True:
            ret, frame = cap.read()
            if not ret:
                break

            if frame_idx % frame_interval == 0:
                timestamp = frame_idx / video_fps

                # Generate visual caption
                caption = self.caption_model.generate(frame)

                # Placeholder for audio/subtitle extraction (would use actual services)
                audio_desc = ""
                subtitle = ""

                frames_data.append(VideoFrame(
                    frame_number=frame_idx,
                    timestamp=timestamp,
                    caption=caption,
                    audio_description=audio_desc,
                    subtitle=subtitle
                ))

            frame_idx += 1

        cap.release()
        return frames_data

    def synthesize_video_narrative(self, frames: List[VideoFrame]) -> str:
        """
        Combine frame captions, audio, and subtitles into cohesive narrative.
        Preserves temporal ordering and scene transitions.
        """
        narrative_parts = []

        for i, frame in enumerate(frames):
            time_str = f"{frame.timestamp:.1f}s"

            # Build description for this segment
            segment = f"[{time_str}] {frame.caption}"
            if frame.subtitle:
                segment += f" (Subtitle: {frame.subtitle})"
            if frame.audio_description:
                segment += f" [Audio: {frame.audio_description}]"

            narrative_parts.append(segment)

        # Join segments with temporal markers
        narrative = "\n".join(narrative_parts)
        return narrative

# Example usage
converter = VideoToTextConverter(
    caption_model=vision_captioner,
    transcription_model=speech_recognizer
)
frames = converter.extract_video_metadata("video.mp4", fps=2)
narrative = converter.synthesize_video_narrative(frames)

Implement adaptive context reduction that samples frames based on query complexity:

from sklearn.feature_extraction.text import TfidfVectorizer
import re

class AdaptiveContextReducer:
    """
    Dynamically reduce video context based on query.
    Complex temporal reasoning → denser sampling.
    Simple visual questions → sparser sampling.
    """

    def __init__(self):
        self.temporal_keywords = [
            "sequence", "order", "after", "before", "first", "last",
            "progression", "timeline", "then", "next", "happens"
        ]
        self.visual_keywords = [
            "color", "appearance", "look", "wear", "object", "shape",
            "size", "position", "location"
        ]
        self.causal_keywords = [
            "why", "because", "cause", "effect", "reason", "result",
            "caused", "leads", "consequence"
        ]

    def classify_query_type(self, query: str) -> str:
        """Classify query to determine sampling strategy."""
        query_lower = query.lower()

        # Check for temporal reasoning queries
        if any(kw in query_lower for kw in self.temporal_keywords):
            return "temporal"
        # Check for causal reasoning
        elif any(kw in query_lower for kw in self.causal_keywords):
            return "causal"
        # Default to visual
        else:
            return "visual"

    def adaptive_sample_frames(self, frames: List[VideoFrame],
                               query: str, total_tokens_budget: int = 2000):
        """
        Sample frames adaptively based on query and token budget.
        """
        query_type = self.classify_query_type(query)

        # Determine sampling rate: temporal needs dense, visual can be sparse
        sampling_rates = {
            "temporal": 0.8,    # Keep 80% of frames
            "causal": 0.6,      # Keep 60% of frames
            "visual": 0.3       # Keep 30% of frames
        }

        sampling_rate = sampling_rates[query_type]
        target_frame_count = max(3, int(len(frames) * sampling_rate))

        # Sample frames uniformly
        if target_frame_count >= len(frames):
            sampled = frames
        else:
            indices = np.linspace(0, len(frames)-1, target_frame_count, dtype=int)
            sampled = [frames[i] for i in indices]

        return sampled

    def reduce_to_context(self, frames: List[VideoFrame],
                         query: str, max_length: int = 2000) -> str:
        """
        Sample frames and synthesize reduced narrative.
        """
        sampled_frames = self.adaptive_sample_frames(frames, query, max_length)

        narrative_parts = []
        for frame in sampled_frames:
            segment = f"[{frame.timestamp:.1f}s] {frame.caption}"
            if frame.subtitle:
                segment += f" | {frame.subtitle}"
            narrative_parts.append(segment)

        narrative = "\n".join(narrative_parts)

        # Truncate if needed
        if len(narrative) > max_length:
            narrative = narrative[:max_length] + "..."

        return narrative

# Example usage
reducer = AdaptiveContextReducer()
reduced_context = reducer.reduce_to_context(frames, "What happens in sequence?")

Implement the LLM-based reasoner that takes text narrative and answers questions:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

class VideoReasoningLLM:
    """LLM-based reasoner for video understanding tasks."""

    def __init__(self, model_name: str = "meta-llama/Llama-2-7b-chat"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)
        self.reducer = AdaptiveContextReducer()

    def answer_question(self, video_narrative: str, question: str,
                       max_answer_length: int = 256) -> str:
        """
        Answer a question about video using language-based narrative.
        """
        # Reduce context adaptively
        reduced_narrative = self.reducer.reduce_to_context(
            video_frames, question
        )

        # Format prompt for LLM
        prompt = f"""Watch this video description and answer the question.

Video Description:
{reduced_narrative}

Question: {question}

Answer: """

        # Tokenize and generate
        inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True)

        with torch.no_grad():
            outputs = self.model.generate(
                inputs["input_ids"],
                max_length=max_answer_length,
                temperature=0.7,
                do_sample=False,
                top_p=0.9
            )

        answer = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Extract answer portion
        answer = answer.split("Answer:")[-1].strip()
        return answer

    def batch_answer_questions(self, video_narrative: str,
                              questions: List[str]) -> List[str]:
        """Answer multiple questions about the same video efficiently."""
        answers = []
        for question in questions:
            answer = self.answer_question(video_narrative, question)
            answers.append(answer)
        return answers

# Example usage
llm_reasoner = VideoReasoningLLM()
video_narrative = converter.synthesize_video_narrative(frames)

questions = [
    "What is the main action in this video?",
    "Describe the sequence of events.",
    "What colors are prominent?"
]

answers = llm_reasoner.batch_answer_questions(video_narrative, questions)
for q, a in zip(questions, answers):
    print(f"Q: {q}\nA: {a}\n")

Evaluate on standard video understanding benchmarks:

def evaluate_on_benchmark(model: VideoReasoningLLM, benchmark_data: List[Dict]):
    """
    Evaluate on video QA benchmarks (Video-MME, MMVU, etc.).
    Expected format: [{"video_path": str, "question": str, "ground_truth": str}]
    """
    correct = 0
    total = len(benchmark_data)

    for item in benchmark_data:
        narrative = converter.extract_video_metadata(item["video_path"])
        predicted = model.answer_question(narrative, item["question"])

        # Simple exact match evaluation
        if predicted.lower().strip() == item["ground_truth"].lower().strip():
            correct += 1

    accuracy = correct / total
    return accuracy

# Run evaluation
accuracy = evaluate_on_benchmark(llm_reasoner, test_benchmark)
print(f"Accuracy: {accuracy*100:.1f}%")

Practical Guidance

When to Use:

• No video-specific training budget or labeled video datasets available
• Need interpretability: users can see exactly what text the model reasoned over
• Deploying to multiple domains: same LLM handles diverse video types
• Complex temporal and causal reasoning tasks (not just visual classification)
• Combining video with other text information (transcripts, subtitles, metadata)

When NOT to Use:

• Real-time video processing required (text conversion adds latency)
• Low-quality video captions available (framework depends on caption quality)
• Subtle visual details matter more than semantic reasoning (motion patterns, micro-expressions)
• Very short videos (<5 seconds) where frame sampling becomes unreliable
• Tasks where explicit video fine-tuning models already dominate benchmarks

Common Pitfalls:

• Poor caption quality kills downstream reasoning; validate captioning model on your domain first
• Over-aggressive context reduction loses important details; start conservative and reduce gradually
• LLM reasoning errors compounded from caption errors; add fact-checking layer if critical
• Ignoring temporal markers in narrative; explicit timestamps help LLM reason about sequence
• Not adapting context reduction to query type; generic sampling misses task-specific needs

Reference

SiLVR: A Simple Language-based Video Reasoning Framework https://arxiv.org/abs/2505.24869