🎯 Object Detection

🧪 ICML2026 · 6 paper notes

📌 Same area in other venues: 📷 CVPR2026 (99) · 🔬 ICLR2026 (31) · 🤖 AAAI2026 (29) · 🧠 NeurIPS2025 (27) · 📹 ICCV2025 (28) · 🧪 ICML2025 (12)

🔥 Top topics: Anomaly Detection ×2

Adversarially Robust Approximate Furthest Neighbor

This theoretical paper provides the first approximate furthest neighbor data structure resistant to adaptive query adversaries. While maintaining a query complexity with \(n\)-dependence similar to Indyk's classical oblivious algorithm, it demonstrates that traditional random projection furthest neighbor algorithms can be broken by adaptive queries.

EARL: Towards a Unified Analysis-Guided Reinforcement Learning Framework for Egocentric Interaction Reasoning and Pixel Grounding

EARL utilizes a two-stage MLLM framework of "coarse interpretation and fine response" to consolidate egocentric interaction reasoning tasks (description + Q&A + pixel mask) into a unified pipeline. The first stage outputs a global interaction description of the full image and treats the last hidden state as a semantic prior. This is injected into the second stage through a novel Analysis-guided Feature Synthesizer (AFS). The system is jointly trained via GRPO with a triple-reward mechanism (format/answer/grounding accuracy), outperforming Seg-Zero by 8.37% cIoU on Ego-IRGBench.

FOCUS: Forcing In-Context Object Localization through Visual Support Constraints and Policy Optimization

FOCUS uses a two-stage training approach—"complete removal of category names + attention mask optimization + GRPO IoU reward"—to force VLMs to perform in-context object localization based on visual support examples rather than semantic priors. The 7B parameter model outperforms 72B models, proving that task-aligned inductive bias is more important than pure scaling.

Mixture Prototype Flow Matching for Open-Set Supervised Anomaly Detection

MPFM replaces the traditional "unimodal Gaussian prototypes" in OSAD with a learnable Gaussian Mixture Model (GMM) prototype space. It uses flow matching to directly regress a velocity field in GMM form, augmented by a mutual information maximization regularization to prevent prototype collapse. The method outperforms all SOTA methods, including DRA, AHL, and DPDL, across 9 industrial and medical AD datasets under the 10/1 anomaly sample setting.

OmniVerifier-M1: Multimodal Meta-Verifier with Explicit Structured Recalibration

Aiming at the issues that multimodal visual verifiers output binary signals (True/False) that are too coarse and that textual explanations are prone to reward-hacking, this paper proposes OmniVerifier-M1. It utilizes symbolic outputs such as bounding boxes as meta-verification rationales instead of text to support rule-based rewards like IoU. Theoretically and experimentally, it proves that decoupling binary judgment and meta-verification into two independent reward streams (rather than a multiplicative joint reward) significantly improves SNR. Ultimately, the verifier is upgraded to an agentic system, M1-TTS, capable of driving region-level self-recalibration.

Testing the Test: Score-Direction Instability in Class-Split Anomaly Detection

The authors point out that "class-split" anomaly detection benchmarks are ill-posed when the anomaly class and the normal mixture distribution overlap in the representation space—AUROC collapses to random or even reverses, with the direction depending on the unknown anomaly class. A training-free "neighborhood class leakage" metric \(L_k\) is proposed to diagnose such benchmark failure before evaluation.