Organizations: Division of Plastic and Reconstructive Surgery, Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, United States · Cornell University, Ithaca, New York, United States · Tulane University, New Orleans, Louisiana, United States · University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States
Purpose: Paired pre- and post-operative photographs are the standard unit of evidence for plastic surgical outcomes, yet no objective metric verifies whether two images of the same patient were captured under conditions consistent for comparison. Approach: We developed a perceptually motivated pipeline that analyzes pre/post pairs across thirteen calibrated sub-metrics, partitioned by unsupervised correlation-structure analysis into five data-driven clusters (photometric, texture / sharpness, pose, illumination direction, and pitch), averaged within each cluster and combined across clusters by a weighted sum into a single consistency score. Each sub-metric is calibrated so that its median difference across published within-patient pairs scores 0.5, which is a reference point and carries no pass/fail meaning. The pipeline was calibrated on 134 matched within-patient published pre/post pairs and evaluated against identical-image pairs, synthetic-perturbation pairs, and 134 mismatched cross-publication pairs. Results: The master consistency score S separated matched from mismatched pairs (sensitivity index d' = 2.15, 95% confidence interval (CI) [1.83, 2.55]; area under the receiver operating characteristic curve AUC = 0.928, 95% CI [0.896, 0.959]), closely matching Gaussian-equal-variance predictions. The three head-pose angles did not fall in one cluster: yaw and roll grouped together while pitch separated. Identical pairs scored at ceiling (S = 0.99) and the master score fell monotonically with perturbation magnitude on all five perturbation axes. Conclusions: The score quantifies photographic comparability, not aesthetic or surgical quality, and provides a freely available web tool for auditing the photographic comparability of pre/post pairs, pending validation against expert judgment.
Reference-free image-to-text evaluators are now standard for scoring image-caption alignment, yet it is unclear whether they respect semantic invariances. We present an invariance probe on five popular evaluators (CLIPScore, PAC-S, UMIC, FLEUR, and a deterministic LLM judge) under semantics-preserving perturbations along three axes -- spatial (flips, context-preserving repositioning, light rotations), object (scale, category), and socio-linguistic framing (cultural/economic adjectives with neutral and length-matched controls). Across curated slices of three detection datasets and three caption evaluation suites, we find consistent non-semantic sensitivities, where benign spatial edits and simple phrasing changes shift scores by ≈6--9% on average, and for systems separated by just 0.7%, these shifts can cause ranking flips in up to ∼37% of cases, particularly under spatial changes. A small human study also supports this finding and confirms that annotators generally judge perturbed pairs as equally correct, so these shifts reflect metric behavior rather than semantic change. We further propose invariance-calibrated scoring, a post-hoc adjustment that roughly halves median absolute sensitivity while retaining correlation with learned caption evaluators.
Amit Agarwal, Hitesh Laxmichand Patel, Meizhu Liu +9
We introduce PerceptionRubrics, a rubric-based evaluation framework that addresses the gap between saturated benchmark scores and real-world brittleness. Shifting evaluation from holistic semantic matching to rigorous atomic auditing, PerceptionRubrics pairs 1,038 information-dense images with over 10,000 instance-specific rubrics. These criteria are derived from golden captions constructed via a novel Circular Peer-Review consensus pipeline and then distilled into a dual-stream system of Must-Right (essential facts) and Easy-Wrong (fine-grained details) rubrics. Crucially, PerceptionRubrics implements a Gated Scoring mechanism: unlike linear averages, failure on mandatory visual facts triggers sharp binary penalties. Extensive evaluation yields critical insights: (1) The Reliability Gap: models often verify fragmented elements correctly yet fail strict conjunctive constraints, exposing brittleness in dense domains; (2) Open-Closed Stratification: contrary to reasoning trends, we reveal a persistent 8% perception deficit between open-source and proprietary frontiers; and (3) Human-Aligned Rigor: our gated metrics substantially out-align conventional benchmarks, validating that strict perceptual fidelity is the prerequisite for reliable generation.
We study full-reference image quality assessment from a machine-centric perspective, where images are evaluated by how well they preserve information for downstream models. We formulate machine-oriented quality as a latent machine utility and approximate it through pairwise predictive-consistency comparisons. To this end, we construct PCMP, a dataset of PSNR-matched distortion pairs labeled by consistency votes from multiple pretrained models. We further propose ML-CLIPSim, a differentiable quality metric built on a frozen CLIP visual encoder, which aggregates intermediate patch-token similarities and global image embeddings. Experiments on machine-preference benchmarks, human-IQA datasets, and learned image compression show that ML-CLIPSim better aligns with machine-oriented preferences than conventional fidelity and perceptual metrics, while remaining competitive for human quality prediction. Used as a compression distortion term, it improves rate--task trade-offs across multiple downstream tasks.