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The paper introduces a counterfactual benchmark to evaluate whether vision models understand physical constraints or merely learn rendering shortcuts. Using Mitsuba 3, it generates 5,500 synthetic images across 500 scene families, each with a clean image and ten violations (shadows, reflections, support, surface response, occlusion). Diagnostic baselines (LLaVA‑1.5‑7B, Qwen2.5‑VL‑7B, InternVL3.5‑8B) achieve 64.0–67.8 % macro‑F1 on a held‑out synthetic test set, but performance drops to 40.8 % for LLaVA‑1.5‑7B under an intervention shift, indicating reliance on rendering cues rather than true physical reasoning.
The paper investigates the blending ratio used in few‑shot adaptation of vision‑language models, which combines a zero‑shot text prototype with the mean of labeled image features. It shows that the theoretically optimal ratio—derived from a closed‑form mean‑squared error minimizer—does not align with the ratio that actually maximizes performance, falling short by an average of 8.5 points. Moreover, a leave‑one‑out estimate on the support set achieves near‑oracle performance, and validation‑free linear probes outperform even oracle‑tuned blends, indicating that the hyperparameter can be set near‑optimally without external validation data.
arXiv:2609.37732v1 Announce Type: cross Abstract: Instruction-based image editors insert objects, restyle scenes and render new viewpoints, but it is unknown which camera they assume when they paint...
FactorSplat introduces a per-scene N‑dimensional Gaussian splatting proxy that allows region‑specific intensity‑to‑RGBA curves to be applied at inference, enabling appearance control in medical volume rendering. The method uses a shared functional encoder and low‑rank per‑Gaussian factors to learn residual appearance while keeping geometry and directional appearance shared across presets. Experiments on seven CT and MR scans show that FactorSplat outperforms region‑aware VEG in mean PSNR and changed‑region error across validation, interpolation, unseen composition, and out‑of‑distribution edits, achieving 1.10 to 1.52 dB PSNR gains and supporting fast TF switches at 524 FPS.
arXiv:2604.25213v2 Announce Type: replace Abstract: OpenAI's GPT-Image-2 has effectively erased the visual boundary between authentic and AI-edited document images: a single number on a receipt can b...
The paper introduces VERDICT, a method for validating optical chemical structure recognition (OCSR) outputs by leveraging agreement among multiple recognizers rather than pixel‑space re‑rendering. On 263 ACS journal images, agreement achieved an AUROC of 0.916, far surpassing the 0.547 AUROC of re‑rendering similarity. VERDICT was applied to PMC Open Access, yielding over 6,000 high‑precision structure labels, and is integrated into SES AI’s Molecular Universe platform for image‑based molecular search.