Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion

• URL: http://arxiv.org/abs/2603.22624v1
• Date: Mon, 23 Mar 2026 22:52:00 GMT
• Title: Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion
• Abstract summary: We introduce a reproducible benchmark that tests intervention-based faithfulness, off-target leakage, runtime, and on Pascal VOC.<n>We propose Dual-Evidence Attribution (DEA), a lightweight correction that fuses evidence with region-level intervention signals.
• Score: 4.726028690278432
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Attribution maps for semantic segmentation are almost always judged by visual plausibility. Yet looking convincing does not guarantee that the highlighted pixels actually drive the model's prediction, nor that attribution credit stays within the target region. These questions require a dedicated evaluation protocol. We introduce a reproducible benchmark that tests intervention-based faithfulness, off-target leakage, perturbation robustness, and runtime on Pascal VOC and SBD across three pretrained backbones. To further demonstrate the benchmark, we propose Dual-Evidence Attribution (DEA), a lightweight correction that fuses gradient evidence with region-level intervention signals through agreement-weighted fusion. DEA increases emphasis where both sources agree and retains causal support when gradient responses are unstable. Across all completed runs, DEA consistently improves deletion-based faithfulness over gradient-only baselines and preserves strong robustness, at the cost of additional compute from intervention passes. The benchmark exposes a faithfulness-stability tradeoff among attribution families that is entirely hidden under visual evaluation, providing a foundation for principled method selection in segmentation explainability. Code is available at https://github.com/anmspro/DEA.

Related papers

• UECP: Uncertainty-Enhanced Collaborative Perception [15.846318270344641]
  Collaborative perception serves as a pivotal solution to enhance the perception capability of individual agents in autonomous driving.<n>Existing methods typically rely on a confidence map, which is correlated with the detection results and thus fails to provide unbiased physical evidence.<n>This paper first proposes an uncertainty map, a physically grounded and unambiguous metric for evaluating perception quality.<n>We develop the Uncertainty-Enhanced Collaborative Perception framework, centered on the Uncertainty-Aware Pyramid Fusion module.
  arXiv  Detail & Related papers  (2026-06-22T08:55:10Z)
• What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
  We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
  arXiv  Detail & Related papers  (2026-06-19T06:04:17Z)
• When Attribution Patching Lies: Diagnosis and a Second-Order Correction [9.338965648455238]
  We show that the dominant error stems from the non-linearities in the downstream network rather than local curvature at the patched component.<n>This insight yields three practical tools: (i) a reliability score to detect untrustworthy estimates, (ii) error bounds quantifying potential attribution mis-specifications, and (iii) a Hessian-vector-product correction that eliminates the leading-order error with only one additional backward pass.
  arXiv  Detail & Related papers  (2026-06-05T05:20:32Z)
• Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation? [4.120238673372104]
  Semi-supervised learning has become a dominant paradigm for reducing annotation costs.<n>We argue that the current progress is clouded by a twofold overconfidence problem.<n>We propose a tri-space principled segmentation framework founded on a dual-axis reliability assessment engine.
  arXiv  Detail & Related papers  (2026-05-25T08:16:40Z)
• EviRCOD: Evidence-Guided Probabilistic Decoding for Referring Camouflaged Object Detection [9.823189831191064]
  Referring Camouflaged Object Detection (Ref-COD) focuses on segmenting specific camouflaged targets in a query image using category-aligned references.<n>We propose EviRCOD, an integrated framework consisting of three core components: (1) a Reference-Guided Deformable (RGDE) that employs hierarchical reference-driven modulation and multi-scale deformable aggregation to inject semantic priors and align cross-scale representations; (2) an Uncertainty-Aware Evidential Decoder (UAED) that incorporates Dirichlet evidence estimation into hierarchical decoding to model uncertainty and propagate confidence across scales; and (3) a Boundary-Aware Refinement Module
  arXiv  Detail & Related papers  (2026-04-13T01:53:45Z)
• FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning [49.65751420291115]
  Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps.<n>We propose FACT-E, a causality-inspired framework for evaluating CoT quality.<n>We show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learnings.
  arXiv  Detail & Related papers  (2026-04-12T15:35:08Z)
• AEGIS: From Clues to Verdicts -- Graph-Guided Deep Vulnerability Reasoning via Dialectics and Meta-Auditing [9.271196825503417]
  Large Language Models (LLMs) are increasingly adopted for vulnerability detection, yet their reasoning remains fundamentally unsound.<n>We present AEGIS, a novel multi-agent framework that shifts detection from ungrounded speculation to forensic verification over a closed factual substrate.<n>It reduces the false positive rate by up to 54.40% compared to leading baselines, at an average cost of $0.09 per sample without any task-specific training.
  arXiv  Detail & Related papers  (2026-03-21T04:12:04Z)
• CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regression [3.6457570471698095]
  We introduce CREDO, a simple "credal-then-conformalize" recipe that combines both strengths.<n> CREDO first builds an interpretable credal envelope that widens when local evidence is weak, then applies split conformal calibration on top of this envelope to guarantee marginal coverage.<n>This separation of roles yields prediction intervals that are interpretable.
  arXiv  Detail & Related papers  (2026-03-06T19:37:21Z)
• Thinking by Subtraction: Confidence-Driven Contrastive Decoding for LLM Reasoning [58.331709210563616]
  Thinking by Subtraction is a confidence-driven contrastive decoding approach.<n>A small subset of low-confidence tokens disproportionately contributes to reasoning errors and unnecessary output expansion.<n>Our method, Confidence-Driven Contrastive Decoding, detects low-confidence tokens during decoding and intervenes at these positions.
  arXiv  Detail & Related papers  (2026-02-20T14:13:22Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• Look As You Think: Unifying Reasoning and Visual Evidence Attribution for Verifiable Document RAG via Reinforcement Learning [55.232400251303794]
  Look As You Think (LAT) is a reinforcement learning framework that trains models to produce verifiable reasoning paths with consistent attribution.<n>LAT consistently improves the vanilla model in both single- and multi-image settings, yielding average gains of 8.23% in soft exact match (EM) and 47.0% in IoU@0.5.
  arXiv  Detail & Related papers  (2025-11-15T02:50:23Z)
• Progressive Uncertainty-Guided Evidential U-KAN for Trustworthy Medical Image Segmentation [11.953394478206581]
  We propose a progressive evidence uncertainty guided attention (PEUA) mechanism to guide the model to focus on the feature representation learning of hard regions.<n>Unlike conventional approaches, PEUA progressively refines attention using uncertainty maps while employing low-rank learning to denoise attention weights.<n>We thus introduce a semantic-preserving evidence learning (SAEL) strategy, integrating a semantic-smooth evidence generator and a fidelity-enhancing regularization term to retain critical semantics.
  arXiv  Detail & Related papers  (2025-10-10T02:58:01Z)
• CLUE: Non-parametric Verification from Experience via Hidden-State Clustering [64.50919789875233]
  We show that correctness of a solution is encoded as a geometrically separable signature within the trajectory of hidden activations.<n>ClUE consistently outperforms LLM-as-a-judge baselines and matches or exceeds modern confidence-based methods in reranking candidates.
  arXiv  Detail & Related papers  (2025-10-02T02:14:33Z)
• TRACEALIGN -- Tracing the Drift: Attributing Alignment Failures to Training-Time Belief Sources in LLMs [7.125400292079228]
  Large Language Models (LLMs) fine-tuned to align with human values often exhibit alignment drift.<n>While prior work has behaviorally characterized alignment failure, little is known about the training-time belief sources underlying these failures.<n>We introduce TraceAlign, a unified framework for tracing unsafe completions back to their root causes in the model's training corpus.
  arXiv  Detail & Related papers  (2025-08-04T05:03:35Z)
• Ev2R: Evaluating Evidence Retrieval in Automated Fact-Checking [11.300523252168327]
  Evtextsuperscript2R combines the strengths of reference-based evaluation and verdict-level proxy scoring.<n>Evtextsuperscript2R consistently outperforms existing scoring approaches in accuracy and robustness.
  arXiv  Detail & Related papers  (2024-11-08T07:05:06Z)
• Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation [74.05906222376608]
  We propose adversarial self-supervision UDA (or ASSUDA) that maximizes the agreement between clean images and their adversarial examples by a contrastive loss in the output space. This paper is rooted in two observations: (i) the robustness of UDA methods in semantic segmentation remains unexplored, which pose a security concern in this field; and (ii) although commonly used self-supervision (e.g., rotation and jigsaw) benefits image tasks such as classification and recognition, they fail to provide the critical supervision signals that could learn discriminative representation for segmentation tasks.
  arXiv  Detail & Related papers  (2021-05-23T01:50:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.