GazeQwen

Gaze-conditioned video understanding with Qwen2.5-VL-7B.

Code: github.com/phamtrongthang123/gazeqwen

GazeQwen injects eye-tracking scanpath information into a frozen Qwen2.5-VL-7B model via lightweight hook-based residual injection, enabling the VLM to leverage human gaze patterns for video QA tasks.

Model Details

• Base model: Qwen2.5-VL-7B-Instruct (frozen)
• Backbone: V-JEPA 2.1 ViT-B/16 384px (frozen, 86M params)
• GazeQwen module: ~10.8M trainable params (Voila Perceiver with Coord-PE gaze input)
• LoRA adapters: rank=8, alpha=16.0 on LLM Q/V projections (~3.5M trainable params)
• Total trainable: ~14.3M params (0.2% of the full model)
• Injection layers: LLM layers [6, 13, 20, 27] out of 28

Files

File	Size	Description
best_model.pt	51MB	GazeQwen checkpoint (f_theta weights + LoRA weights)

The V-JEPA 2.1 backbone checkpoint must be downloaded separately:

wget https://dl.fbaipublicfiles.com/vjepa2/vjepa2_1_vitb_dist_vitG_384.pt

Checkpoint Format

checkpoint = torch.load("best_model.pt", map_location="cpu", weights_only=False)
# Keys:
#   "state_dict"      - GazeQwen f_theta module weights
#   "config"          - f_theta architecture config dict
#   "lora_state_dict" - LoRA adapter weights for Qwen2.5-VL LLM
#   "lora_config"     - {"rank": 8, "alpha": 16.0}

Quick Start

import torch
from gazeqwen.model import GazeLens
from gazeqwen.hooks import GazeLensContext, register_gazelens_hooks
from gazeqwen.lora import apply_lora, load_lora_state_dict
from gazeqwen.vjepa_features import VJEPAFeatureExtractor
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

# Load frozen Qwen2.5-VL-7B
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Qwen/Qwen2.5-VL-7B-Instruct",
    torch_dtype="auto", device_map="auto", attn_implementation="eager",
)
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct")

# Load GazeQwen checkpoint
checkpoint = torch.load("best_model.pt", map_location="cpu", weights_only=False)
f_theta = GazeLens(**checkpoint["config"])
f_theta.load_state_dict(checkpoint["state_dict"])

# Apply LoRA
lora_cfg = checkpoint["lora_config"]
apply_lora(model, rank=lora_cfg["rank"], alpha=lora_cfg["alpha"])
load_lora_state_dict(model, checkpoint["lora_state_dict"])

# Register hooks and run inference
ctx = GazeLensContext(f_theta.to(model.device))
register_gazelens_hooks(model, ctx)
vjepa = VJEPAFeatureExtractor(device=model.device)

# scanpath: (N, 4) tensor [x, y, midpoint_time, duration]
# frame_times: list of float timestamps
# features: list of (H*W, 768) tensors from vjepa.extract_from_raw_frames()
# with ctx.active(scanpath, frame_times, backbone_features=features):
#     output = model(**inputs)

Architecture

Input Video + Eye Tracking Scanpath
         │                │
    Qwen2.5-VL        V-JEPA 2.1
    (frozen)           (frozen)
         │                │
         │          ┌─────┴─────┐
         │          │  Coord-PE │ ← Fixation (x,y) encoding
         │          │  Gaze     │
         │          └─────┬─────┘
         │                │
         │     ┌──────────┴──────────┐
         │     │  Voila Perceiver    │ ← 32 latents, 2 blocks
         │     │  (per-layer × 4)   │   4 independent modules
         │     └──────────┬──────────┘
         │                │
    LLM Layers ◄──── Residual Bias  (added at layers 6, 13, 20, 27)
         │
    + LoRA (Q/V)
         │
      Answer (A/B/C/D)

Results

Evaluated on test set (EgoExo+EGTEA, 249 videos with gaze annotations):

Task	no_gaze	GazeQwen	Delta	p-value
OTP (Object Transition Prediction)	37.3%	64.4%	+27.1%	0.002
NFI (Non-Fixated Identification)	36.9%	70.2%	+33.3%	<0.001
FAP (Future Action Prediction)	33.1%	45.9%	+12.8%	0.005
OAR (Object Attribute Recognition)	51.2%	81.3%	+30.1%	<0.001
OI-E (Object Identification Easy)	54.8%	62.2%	+7.4%	0.038
OI-H (Object Identification Hard)	50.0%	76.2%	+26.2%	<0.001
GTA (Gaze-Triggered Alert)	65.6%	63.6%	-1.9%	0.729
OAA (Object Appearance Alert)	51.4%	74.3%	+22.9%	<0.001
Overall	50.4%	70.3%	+20.0%	<0.001

Statistically significant improvement on 7 out of 10 tasks (McNemar test, p<0.05).

Training

• Data: 249 videos with eye-tracking from EgoExo and EGTEA datasets, 8521 QA pairs across 10 tasks
• Split: 70/15/15 by video (deterministic seed=42)
• Optimizer: AdamW (lr=3e-4, weight_decay=1e-2)
• Schedule: Linear warmup (20 steps), cosine decay
• Epochs: 3 (early stopping on validation accuracy)
• Hardware: 1x NVIDIA A100 80GB

Limitations

• Requires eye-tracking/gaze data at inference time — not applicable to videos without gaze annotations
• Proactive task GTA shows no improvement over baseline (gaze signal may not help with temporal alert detection)
• GSM and SR tasks are underpowered in the test set (n=23 and n=31)

License

Apache-2.0