Simplify model card; BMVC 2026 citation

README.md

@@ -11,47 +11,33 @@ tags:
 - peft
 - cvpd
 - self-distillation
-- self-evolving
 - multimodal
 - vision-language
 - lmm
 - ocr
-- visual-perception
 - qwen3-vl
 - unsupervised
 ---
 
-# CVPD: Contrastive Counterfactual Visual Process Distillation
+# CVPD
 
-This is the CVPD LoRA adapter for `Qwen/Qwen3-VL-4B-Instruct`, from our paper
+This is the CVPD LoRA adapter for `Qwen/Qwen3-VL-4B-Instruct`, from our BMVC 2026 paper
 [Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots](https://github.com/mbzuai-oryx/CVPD).
 
-CVPD is a fully self-contained framework for dense, on-policy, token-level visual
-self-distillation. Prior visual distillation methods build their privileged context
-with external tools — segmentation models, region proposal networks, annotation
-pipelines, or stronger annotators such as GPT-4o. CVPD removes that dependency
-entirely: the privileged context is recovered from the model's *own* counterfactual
-behavior over unlabeled images, with no captions, bounding boxes, labels, reward
-models, verifiers, or teacher models at any stage.
-
-The key insight is that a region carries useful privileged information when zooming
-into it *changes and sharpens* the model's answer distribution, while erasing it
-leaves the full-image behavior largely unchanged. Such regions are **visual blind
-spots**: the model can already perceive them, but fails to exploit them under
-full-image conditioning.
-
-- **Phase 1 — Counterfactual Blind-Spot Discovery.** The model generates its own
-  fine-grained question and probe answer per image, proposes candidate regions from
-  three tracks (self-grounding, a 3x3 grid, and a 2x2 grid), and keeps only regions
-  passing a three-gate Counterfactual Criterion: latent capability divergence
-  (the crop moves the answer distribution), default perceptual invariance (the
-  ghosted image does not), and epistemic sharpening (the crop lowers entropy).
-- **Phase 2 — Contrastive Self-Distillation.** Each retained region instantiates
-  four policies from the same backbone: an online student, a crop-conditioned
-  positive teacher, a ghost-conditioned negative teacher, and a frozen reference
-  policy. The student is pulled toward the crop teacher (latent transfer), pushed
-  away from the ghost teacher (contrastive ranking, margin `m=0.1`), and anchored
-  to the reference policy (adaptive KL, target `0.03`).
+Vision-language models often miss details they can actually see. Ask about a small
+sign in a photo and the model gets it wrong, but crop to that sign and it answers
+correctly. The information was there, the model just didn't attend to it.
+
+CVPD trains models to fix this using nothing but the model itself. We show it an
+unlabeled image, let it write its own question, and then check two things: does
+zooming into a region change and sharpen its answer, and does blurring that region
+leave its answer alone? When both hold, we've found a blind spot: something the
+model can perceive but doesn't use. We then train the model on the full image to
+behave the way it did on the crop, and away from how it behaved on the blurred
+version.
+
+No captions, no bounding boxes, no labels, no reward models, no bigger teacher
+model. Just the model and a pile of raw images.
 
 ## Usage
 
@@ -84,41 +70,15 @@ with torch.inference_mode():
 print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
 ```
 
-## Results (Qwen3-VL-4B-Instruct)
-
-| Benchmark | Base | CVPD |
-| --- | --- | --- |
-| OCRBench | 81.70 | 84.35 |
-| MMStar Fine-Grained Perception | 60.86 | 63.35 |
-| MMStar Logical Reasoning | 62.96 | 65.25 |
-| MMStar Instance Reasoning | 69.83 | 71.20 |
-| InfoVQA | 77.73 | 79.15 |
-| ScienceQA | 87.51 | 89.05 |
-| AI2D | 80.10 | 82.35 |
-| CV-Bench | 85.45 | 87.15 |
-| RealWorldQA | 71.24 | 73.45 |
-| MMBench-EN | 83.51 | 84.60 |
-| MME-Perception | 1702.9 | 1715.5 |
-| SEED-Image | 78.05 | 78.20 |
-
-The largest gains appear on benchmarks requiring precise localized attention —
-OCRBench (+2.65), MMStar Fine-Grained Perception (+2.49), and MMStar Logical
-Reasoning (+2.29). CVPD is the only method in our comparison that improves over
-the base model on every benchmark without regressing on any, at both the 4B and
-8B scales. Full results, ablations, and the 8B model are in our paper.
-
 ## Training
 
-- Base: `Qwen/Qwen3-VL-4B-Instruct`, vision encoder frozen.
-- LoRA: `r=32`, `alpha=64` on the attention and MLP projections of the language model backbone.
-- Optimizer: AdamW, `lr=2e-5`, weight decay `0.01`, gradient clipping `1.0`, bfloat16.
-- Objective: latent transfer + contrastive ranking (`lambda_rank=0.5`, margin `m=0.1`)
-  + KL anchor (`beta_0=1e-3`, adapted online to target KL `kappa=0.03`).
-- Momentum teacher pair via EMA of the LoRA parameters (`alpha=0.05`).
-- Discovery thresholds: `tau_crop = tau_ghost = 0.05`. All views resized to 448x448.
-- Data: 15,000 raw, unlabeled images (10,000 natural-scene + 5,000 reasoning-domain),
-  yielding ~2,590 curated blind-spot tuples (17.2% pass rate). No captions,
-  bounding boxes, labels, answers, or reward signals are used at any stage.
+- Base model: `Qwen/Qwen3-VL-4B-Instruct`, with the vision encoder frozen.
+- LoRA: `r=32`, `alpha=64`, `dropout=0.05`, on the attention and MLP projections
+  of the language model.
+- AdamW, learning rate `2e-5`, weight decay `0.01`, gradient clipping `1.0`, bfloat16.
+- One epoch over the curated blind-spot pool.
+- Data: 15,000 raw unlabeled images, which the discovery pipeline narrows down to
+  about 2,590 training examples.
 
 ## License
 
@@ -127,11 +87,11 @@ Apache 2.0.
 ## Citation
 
 ```bibtex
-@article{venkatraman2026cvpd,
-  title   = {Perception Before Supervision: Self-Contained Visual Distillation
-             from Counterfactual Blind Spots},
-  author  = {Venkatraman, Shravan and Thawakar, Omkar and Thawkar, Ritesh and
-             Shaker, Abdelrahman and Anwer, Rao Muhammad},
-  year    = {2026}
+@inproceedings{cvpd,
+author    = {Venkatraman, Shravan and Thawakar, Omkar and Thawkar, Ritesh and Shaker, Abdelrahman and Muhammad, Rao},
+title     = {Perception Before Supervision: Self-Contained Visual Distillation from Counterfactual Blind Spots},
+booktitle = {37th British Machine Vision Conference 2026, {BMVC} 2026, Lancaster, UK, November 23-26, 2026},
+publisher = {BMVA},
+year      = {2026}
 }
 ```