Shorten and move codec patch selection section down

Co-authored-by: anxiangsir <31175974+anxiangsir@users.noreply.github.com>

Author: Copilot

README.md

@@ -29,11 +29,11 @@
 ## 📖 Table of Contents
 
 - [Introduction](#-introduction)
-- [Codec Style Patch Selection](#-codec-style-patch-selection)
 - [Setup](#-setup)
 - [Quick Start](#-quick-start)
 - [Training](#-training)
 - [Evaluation](#-evaluation)
+- [Codec Style Patch Selection](#-codec-style-patch-selection)
 - [Contributors](#-contributors)
 - [License](#-license)
 - [Documentation](#-documentation)
@@ -104,59 +104,6 @@ Standard contrastive learning methods (e.g., CLIP) are fundamentally constrained
 
 ---
 
-## 🎬 Codec Style Patch Selection
-
-OneVision Encoder implements a codec-inspired patch selection mechanism that intelligently identifies and processes only the most informative patches from video frames. This approach is inspired by HEVC (High-Efficiency Video Coding) and enables efficient video understanding by focusing computation on temporally salient regions.
-
-### Implementation in `llava_next`
-
-The codec style patch selection is implemented across several key components in the [`llava_next`](llava_next) directory:
-
-### 1. Patch Selection Pipeline
-
-Location: [`Compressed_Video_Reader/tool/`](llava_next/Compressed_Video_Reader/tool/)
-
-- **Stage 1** ([`stage1.py`](llava_next/Compressed_Video_Reader/tool/stage1.py)): Extracts codec information from videos
-  - Computes fused Motion Vector (MV) and Residual energy per frame
-  - Performs global top-k selection over temporal-spatial patches
-  - Outputs `visidx_thw.npy` containing selected patch indices
-
-- **Stage 2** ([`stage2.py`](llava_next/Compressed_Video_Reader/tool/stage2.py)): Packs selected patches into training format
-  - Generates mosaic images from selected patches
-  - Creates `positions_thw.npy` files with [t, h, w] coordinates for each patch
-
-### 2. Training Integration
-
-Location: [`llava/train/train.py`](llava_next/llava/train/train.py)
-
-The training pipeline loads codec patch positions (lines 1267-1268):
-```python
-if "positions_thw" in sources[0]:
-    patch_positions = torch.tensor(np.load(sources[0]["positions_thw"])).unsqueeze(0)
-```
-
-### 3. Model Architecture
-
-Location: [`llava/model/llava_arch.py`](llava_next/llava/model/llava_arch.py)
-
-The model passes patch positions to the vision encoder (line 199):
-```python
-def encode_images(self, images, grid_thw=None, patch_positions=None):
-    ...
-    image_features = vision_tower(images, patch_positions=patch_positions)
-```
-
-### How It Works
-
-1. **Temporal Saliency Detection**: Analyzes all frames to identify regions with motion, appearance variations, and semantic changes
-2. **Selective Patch Extraction**: Extracts only salient patches in a zigzag order, achieving 75-98% compression
-3. **3D Position Encoding**: Uses [t, h, w] coordinates to maintain spatiotemporal relationships
-4. **Efficient Processing**: Processes many frames sparsely instead of few frames densely
-
-For detailed usage instructions, see the [LLaVA-Next README](llava_next/README.md).
-
----
-
 ### LMM Probe Results
 
 We train the model on a mixed dataset comprising 740K samples from LLaVA-OneVision and 800K samples from LLaVA-Video SFT, proceeding directly to Stage-2 fine-tuning. Following a streamlined native-resolution strategy inspired by LLaVA-OneVision, input frames that match the model’s native resolution are fed directly into the network without tiling or cropping, allowing us to fully evaluate the ViT’s native-resolution modeling capability.
@@ -514,6 +461,22 @@ bash shells_eval_ap/eval_ov_encoder_large_2kpatches_codec.sh
 
 </details>
 
+---
+
+## 🎬 Codec Style Patch Selection
+
+The codec-inspired patch selection mechanism identifies and processes only the most informative patches from video frames, inspired by HEVC video coding.
+
+**Implementation in [`llava_next`](llava_next):**
+
+- **Pipeline**: [`Compressed_Video_Reader/tool/`](llava_next/Compressed_Video_Reader/tool/) - Stage 1 extracts codec info (MV/Residual energy), Stage 2 packs patches with position coordinates
+- **Training**: [`llava/train/train.py`](llava_next/llava/train/train.py) - Loads `positions_thw.npy` patch positions
+- **Model**: [`llava/model/llava_arch.py`](llava_next/llava/model/llava_arch.py) - Passes positions to vision encoder
+
+For detailed usage, see the [LLaVA-Next README](llava_next/README.md).
+
+---
+
 ## 👥 Contributors
 
 <!-- Add contributor list here -->