NeurIPS 2026 论文列表

Self-Supervised Learning (SSL) has achieved impressive success in learning semantic visual representations, yet the underlying principles driving this success remain underexplored. In this work, we hypothesize that common SSL pretext tasks implicitly model object co-occurrence statistics, a fundamental cue for visual learning. To test this, we curate three datasets of segmented objects from existing vision benchmarks using a state-of-the-art segmentation model. Through experiments across many SSL models, we reveal a hierarchical encoding of semantic information: while the visual backbone captures object categories, the projection head specializes in encoding co-occurrences between object categories. As a result, the projection head outperforms the backbone in aligning with human judgments of inter-category similarity. Furthermore, by controlling co-occurrence patterns during pre-training, we demonstrate that encoding object co-occurrences can significantly accelerate the emergence of category-level representations. Our findings uncover a previously hidden learning principle in SSL and suggest a path toward designing more effective pretext tasks by explicitly leveraging object co-occurrence structure.

Decentralized learning (DL) is an emerging machine learning paradigm where nodes collaboratively train models without a central server. However, the collaborative nature of DL makes it vulnerable to backdoor attacks, where a model is taught to behave normally on standard inputs while executing hidden, malicious actions when encountering data with specific triggers. Backdoor attacks in DL remain understudied and existing defenses often overlook DL constraints. We introduce Argus, a novel backdoor detection framework native to DL that requires neither a central coordinator nor prior knowledge of the trigger. In Argus, honest nodes locally analyze received model updates to identify potential backdoor triggers. Nodes then collectively share their triggers with their neighbors and use a structural similarity metric to separate true backdoors from false alarms induced by data heterogeneity. A key insight is that false positive triggers exhibit inconsistencies across participants while true positive ones show consistent patterns. Model updates that fail this collaborative test are rejected, and persistently malicious senders are eventually evicted. We provide the first theoretical convergence guarantees for a DL-specific backdoor detection mechanism, showing that filtering out suspicious model updates with high probability preserves a convergence rate comparable to standard DL. We implement and evaluate Argus on three standard datasets and against three state-of-the-art baselines. Across settings, Argus reduces attack success rates by up to 90 points compared to no defense, while preserving model utility within 5 percentage points of an omniscient oracle. Furthermore, the effectiveness of Argus compared to baselines improves as data heterogeneity increases.

Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outputs are typically static monolithic assets with limited editability and physical interaction. This restricts their use in immersive content creation and embodied simulation, where generated worlds must be actively modified and manipulated. To tackle this challenge, we present WorldAct, a framework that converts static generated 3D worlds into editable and interaction-ready scenes. WorldAct uses a multimodal agent to guide scene decomposition, identify actionable objects, reconstruct geometrically aligned object-level meshes for interaction, and restore the residual background via 3D inpainting. The resulting scenes support object-level editing, collision-aware manipulation, and embodied task execution while preserving global scene coherence. Experiments show that WorldAct enables richer interaction scenarios than the original generated scenes, suggesting a practical path toward editable and interactive 3D world models.

We introduce WildBox, a dataset and benchmark for monocular 3D detection of wildlife from drone video, comprising 237,505 3D bounding box annotations across seven African savanna species grouped into six benchmark classes. Annotations follow a KITTI/Omni3D-compatible format in a per-segment scale-normalised camera frame, with instance identities maintained across each segment. We evaluate two open-vocabulary monocular 3D architectures, OVMono3D-LIFT and DetAny3D, under zero-shot, ground-truth 2D box prompt, and supervised fine-tuning protocols. Open-vocabulary 2D foundation models provide usable zero-shot wildlife localisation (50.55 AP@50), but zero-shot 3D detection collapses to 0.00 AP across both architectures and every 2D-input condition tested, including ground-truth 2D box prompts—isolating the failure to the 3D stage. Fine-tuning on WildBox recovers performance to 8.68$\pm$0.47 AP-BEV@0.50 and 13.17$\pm$0.69 AP3D macro. Depth contributes 84\% of normalised Hausdorff distance after fine-tuning and over 99\% in zero-shot, identifying monocular aerial depth as the dominant open problem in this regime. A coarse-to-fine curriculum—pretraining on a merged zebra class before fine-tuning on the Grévy's/plains split—improves macro 3D performance with less total compute, with the largest gains on the two zebra subclasses. WildBox is released with video-level splits, evaluation code, and baseline checkpoints to enable progress in 3D wildlife perception from drone video.

For $K=2$ regimes, scalar thresholding of the Bayesian posterior is Bayes-optimal. For $K \geq 3$, the posterior evolves on a $(K-1)$-dimensional simplex $\Delta^{K-1}$, yet virtually all deployed detection systems reduce it to a scalar score without theoretical justification. We resolve this question completely. We introduce **Extended Decision Sufficiency (EDS)**—three linear constraints on emission densities and transition dynamics (Rank-One Emissions, Markov Factorisation, Normal Factorisation), verifiable in $O(K^2)$ operations from model parameters alone—and prove the following exact characterisation: **Main result.** EDS holds if and only if the Bayes-optimal stopping value function is constant on every level set of a scalar statistic $\phi : \Delta^{K-1} \to \mathbb{R}$. Sufficiency follows from algebraic closure of the Bellman operator $\mathcal{B}$ under EDS. Necessity is proved by a coupling-based fixed-point contradiction: for each failure mode we exhibit an explicit belief pair with equal $\phi$-values but a strictly positive total-variation gap on predictive densities, which propagates to a value-function gap via the Lipschitz bound on $\mathcal{B}$ and the $\rho$-contraction of $\mathcal{T}$, without assuming any structure on $V$ beyond continuity. Three consequences are sharp and quantitative: * **(i)** When EDS fails, *every* scalar rule—regardless of architecture, capacity, or training data—incurs a strictly positive, computable, algorithm-independent lead-time loss. * **(ii)** When EDS holds, optimal expected lead time is given in closed form by a hitting-time functional of a scalar Markov chain. * **(iii)** Under approximate EDS with deviation $\eta$, performance degrades at rate $O(\eta/(1-\rho))$, making $\eta$ a practical model-diagnostic with direct performance implications. Across 72 synthetic configurations and the CICIDS2017 benchmark, predicted and observed lead times agree within 0.4 steps (MAE). A controlled experiment with matched parameter counts confirms that the lead-time advantage is structural, not a regularisation artefact, thereby validating the theory as the sole causal mechanism.

AI-generated videos are becoming increasingly realistic, yet most detection methods focus on spatial artifacts within individual frames and require synthetic training data from known generators, causing them to fail on unseen generators. We identify two previously unexplored temporally distributed forensic microstructures that reveal whether a video was AI-generated. These traces arise from how a video's visual field and motion evolve over time, capturing subtle deviations that generators introduce when approximating real-world temporal dynamics. To expose them, we train two simple predictors on real video only, modeling visual evolution (VE-FSD) and motion evolution (ME-FSD), and analyze their prediction residuals. We fit 3D spatio-temporal autoregressive models to these residuals, producing compact descriptors we call Video Forensic Self-Descriptions (VFSD). VFSDs of real videos naturally cluster together and separate from AI-generated ones, enabling zero-shot detection and source attribution without synthetic training data. Experiments across multiple datasets and 35 generators show VFSD achieves state-of-the-art performance on both tasks.

Anomaly detection (AD) is a critical task in machine learning and scientific discovery. Existing methods that operate on learned embeddings typically define anomalies through local density, isolation heuristics, or distance from a fitted distribution, making them sensitive to neighborhood scale, partitioning choices, or restrictive distributional assumptions. In this work, we introduce a new paradigm by formulating anomaly detection in terms of dataset diversity. We propose the Vendi Anomaly Score (VAS), which detects anomalies by quantifying how much a sample changes the diversity of the dataset, as measured by the Vendi Score, when removed. VAS captures both local redundancy and global data structure without relying on density estimation or partitioning heuristics. Moreover, VAS requires no hyperparameter tuning, instead adapting automatically to the spectral structure of the data. VAS is non-parametric and scales linearly with dataset size. Across 148 benchmark embedding datasets derived from 10 source datasets and 4 embedding architectures, VAS achieves state-of-the-art AD performance. We further validate VAS on large-scale ImageNet experiments, where it remains robust as both contamination rate and dataset scale increase.

Despite rapid progress in synthetic image detection, detectors that perform well on known generators often become brittle when the source model, post-processing pipeline, or scene composition changes. A key reason is that standard binary supervision teaches models to separate training distributions rather than to identify authenticity-relevant evidence: low-level artifacts become shortcuts, while higher-level semantic implausibilities remain weakly grounded. To address this limitation, we propose **UniFPL**, a unified forensics preference learning framework that converts multimodal large language model (MLLM) forensic knowledge into image-specific preference supervision. UniFPL constructs forensic preference data from both controlled artifact synthesis and observed artifact harvesting, then learns from list-level evidence rankings that encode which visual-textual cues are more diagnostic of synthetic manipulation. On top of a CLIP-based discriminative detector, UniFPL jointly optimizes authenticity classification, artifacts-aware preference alignment, and semantic structure regularization, allowing the model to inherit contextual forensic reasoning while retaining the stable and efficient inference of a discriminative encoder. Across eleven curated and in-the-wild benchmarks, UniFPL achieves an average balanced accuracy of 93.4\% and improves the worst-case accuracy from 81.4\% to 86.1\% over the strongest baseline, demonstrating stronger cross-generator generalization and robustness to real-world image degradations.

Reliable map-free LiDAR relocalization requires not only accurate and efficient pose estimation but also trustworthy confidence estimation for detecting localization failures. However, estimating reliability to ensure trustworthy LiDAR relocalization remains largely unexplored. Also, existing LiDAR pose regression methods suffer from accuracy and efficiency trade-off. To address this limitation, we propose \textbf{SPAR}, a model-agnostic confidence estimator that reformulates localization reliability as scan-pose alignment. Given a query scan and a predicted pose, SPAR measures their compatibility in a learned embedding space and explicitly optimizes confidence scores to correlate scan with pose. In addition, we introduce \textbf{VeLoc}, an efficient and accurate pose regressor based on flow-matching for efficient pose refinement. Experiments on the Oxford and NCLT datasets demonstrate that the proposed framework achieves a favorable balance between efficiency and accuracy for LiDAR relocalization while enabling reliable localization failure detection across different localization backbones.

To ensure a formal guarantee that fully human-written text streams are *not falsely accused* as machine-generated text (MGT), the online betting framework (e-values) has been successfully applied on various existing detectors. However, current two-sample e-value methods fail in two real-world application scenarios: the false positive rate (FPR) is often out of control and the test power drops drastically when (1) the reference domain is similar but not exactly same as test domain (cross-domain), or (2) proxy model of detector is outdated compared to the modern source model. We identify the root cause as an online calibration dilemma: two-sample optimization and betting relies on a null-calibrating offset that is unidentifiable from unlabeled test streams under shift. We resolve this with a triple-sample betting framework (TriBet) that conduct online MGT detection as relative testing against both human and machine references using a witness score, and introduces a conformal test-inclusive domain-shift betting strategy to achieve FPR control without fragile online calibration. We show that TriBet is a level-$\alpha$ sequential test with asymptotic power one, and characterize its expected stopping time. Empirically, we evaluate on 15 source LLMs, 8 proxy models and mixed-document streams, TriBet consistently delivers faster and more powerful detection than both oracle and online betting baselines while keeping FPR strictly controlled.

Controlling the motion of multiple objects in image-to-video (I2V) generation requires preserving object identities while enforcing adherence to distinct target trajectories. This becomes particularly challenging as the number of objects increases and their paths intersect or occlude one another. Existing approaches entangle multiple trajectories within a shared, dense conditioning signal, making object-level correspondence difficult to preserve in crowded scenes. We depart from this paradigm and enforce a strict, per object spatial constraint that isolates instances independently. Our method, TrajLoc, achieves this directly within the attention layers by substituting the cross-attention weights of each object token with a Gaussian heatmap centered on its target location at every frame. The same per object token interface carries trajectory and depth through a learned embedding and preserves identity by encoding first frame appearance in place of an object token. Evaluations across six datasets, featuring up to 20 simultaneously controlled objects and out of distribution real world scenes, demonstrate that our method consistently improves both visual fidelity and trajectory adherence. Applied to two architecturally distinct backbones (CogVideoX 5B and WaN 2.1 14B), our approach achieves average gains of +4.3 dB PSNR and a 51\% reduction in trajectory end point error compared to the strongest baselines. Code will be made publicly available upon publication.

Open World Object Detection (OWOD) requires a detector to recognize known categories, detect unknown objects, and incrementally learn new classes. Recent work such as OW-OVD adapts a pre-trained Vision-Language Object Detector (VLOD) to OWOD through fine-tuning. However, this approach demands costly backpropagation and often degrades the detector's own zero-shot performance on known categories, calling into question whether training is necessary. In this paper, we propose CAVE (Clustering in Attribute-Visual Embedding space), a training-free framework that adapts a VLOD to OWOD without any parameter updates. From a single forward pass, CAVE collects per-class visual and attribute statistics via von Mises-Fisher-based clustering, along with attribute co-occurrence patterns, without any gradient computation. At inference, CAVE fuses the original VLOD's text-based scores with MAS (Mixture-Aggregate Score) derived from the collected statistics for known-class detection, while unknown objects are identified by AOS (Attribute Objectness Score) that combines superclass responses with attribute co-occurrence patterns. Experiments on M-OWODB show that CAVE outperforms OW-OVD by +16.2 known-class mAP (Task 4) and +28.0 average unknown recall, while surpassing the zero-shot VLOD across all incremental tasks.

Although advancements in face landmark detection (FLD) methods continue to push performance boundaries, they overlook two major functional limitations: (1) different network parameters need to be trained independently for each "N-point" benchmark dataset, and (2) a model trained on an "N-point" dataset reliably outputs only the N landmarks. In our work, we first conceptualize Face Part-Anchored Landmark Positions (FPALPs), wherein each landmark is treated as a progression value between zero (start) and one (end) along a face part's contour. Every landmark can be expressed in the FPALP format, irrespective of its source dataset, hence unlocking the ability to unify all "N-point" datasets into a single dataset. Secondly, we represent each landmark with an FPALP-based query, refine it progressively with a cross-modality decoder, and predict its coordinates based on the final representation. Our approach, called Unified Dynamic FLD, embodies these two design choices and streamlines the landmark detection pipeline by enabling (1) a single model to learn on any number of "N-point" datasets, and (2) yield any number of specific landmark predictions by loading the designated landmark queries at runtime. Extensive experiments on multiple benchmark datasets show that our method delivers these benefits while remaining competitive with, and in several cases outperforming existing state-of-the-art methods.

Graph-level anomaly detection (GLAD) aims to identify graphs that deviate significantly from the majority and plays a crucial role in applications such as molecular analysis and fraud detection. While recent advances have achieved competitive performance, existing approaches mainly follow a dataset-specific paradigm, requiring substantial in-domain data and training cost when applied to new application scenarios. This limits their scalability and practicality in real-world applications where labeled data is scarce and diverse domains are continuously emerging. In this paper, we investigate the problem of generalist GLAD, which seeks to learn a single unified model capable of detecting anomalous graphs across multiple domains with minimal supervision on the target data. This problem introduces key challenges, including learning transferable patterns from heterogeneous graph datasets and effective adaptation under limited supervision. To address these challenges, we propose GenGLAD, a generalist GLAD approach that leverages discriminative subgraph extraction as the core mechanism to learn transferable anomaly knowledge and enable efficient adaptation to new domains. Specifically, GenGLAD employs a graph neural network-based discriminative subgraph extraction model to learn the anomaly-discriminative substructures that distinguish anomalous and normal graphs across diverse domains. During the test-time adaptation phase, multi-dimensional discrepancies between the extracted subgraph and multi-level contextual information are calculated to characterize anomalies from multiple perspectives. For efficient adaptation, a lightweight scoring module is introduced to automatically decide the most informative signals, improving robustness against misleading patterns. Extensive experiments on diverse benchmark datasets demonstrate that the proposed approach achieves strong performance, superior generalization ability, and improved data efficiency compared to existing methods.

Adapting CLIP for anomaly detection on unseen objects has shown strong potential in a zero-shot manner. Existing methods typically rely on a single textual space to align with visual semantics across diverse objects and domains. The indiscriminate alignment hinders the model from accurately capturing anomaly semantics. We propose TokenCLIP, a token-wise dynamic framework that aligns each visual token with learnt textual subspaces corresponding to its visual characteristics. However, explicitly assigning a unique learnable textual space to each token is computationally intractable and prone to insufficient optimization. We instead expand the token-agnostic textual space into a set of orthogonal subspaces, and then dynamically assign each token to a subspace combination guided by semantic affinity, which jointly supports customized and efficient token-wise adaptation. To this end, we formulate dynamic alignment as an optimal transport problem, where all visual tokens in an image are transported to textual subspaces under the cross-modal cost matrix. \textbf{The marginal constraint and minimal cost objective of OT ensure sufficient optimization across subspaces and encourage them to focus on different semantics.} Solving the problem yields a transport plan that adaptively assigns each token to semantically relevant subspaces. Extensive experiments show that the textual subspaces naturally specialize in different semantics, such as foreground and background, to promote fine-grained anomaly learning. The comparison between baselines shows the superiority of TokenCLIP.

Recent advances in Vision-Language Models (VLMs) have achieved impressive performance across many tasks, yet prior studies report unsatisfactory performance when applying large language or multimodal models to finding abnormal patterns in sequential data. Public anomaly detection benchmarks typically provide interval annotations but not natural-language rationales, making it difficult to fine-tune VLMs to produce grounded, interpretable decisions. To address this gap, we construct VisAnomBench, an explanation-augmented curated benchmark built from public time-series datasets and augmented with high-quality anomaly explanations selected from multiple large VLMs using fine-grained, task-specific rewards. Building on this benchmark, we present VisAnomReasoner, a parameter-efficient VLM for time-series anomaly detection. Experimental results on VisAnomBench show that VisAnomReasoner achieves more accurate anomaly localization and consistently outperforms all baselines, with improvements of at least 21.23 and 23.87 percentage points in precision and F1, respectively. Additional experiments on the TSB-AD-U benchmark demonstrate strong cross-benchmark generalization, with VisAnomReasoner improving precision and F1 by 9.57 and 13.39 percentage points, respectively. Dataset, code, and model weights will be open-sourced.

With the rapid advancement of generative models, AI-generated images (AIGIs) have become increasingly realistic, posing new challenges for reliable detection. In this work, we revisit the root causes of poor generalization in AIGI detectors from the perspective of shortcut learning. We find that detectors often rely on spurious semantic cues or a single prominent artifact, causing the feature space to collapse toward low diversity and severely limiting generalization. To characterize this issue, we propose the Generative Artifact Manifold Clustering (GAMC) hypothesis, which shows that although generative artifacts are multi-dimensional, different generators occupy distinct distributions along these dimensions, making detectors that rely on local artifact cues inherently fragile. To address this, we shift AIGI detection from “single-cue reliance” to “multi-dimensional discrimination” and introduce the Semantic–Artifact Self-Adversarial Feature Learning (SA-AFL) framework. SA-AFL progressively disentangles semantics from artifacts and employs a self-adversarial mechanism to balance learning across artifact dimensions, enabling the model to retain richer and more shared generative features. As a result, SA-AFL reduces shortcut reliance and learns more transferable evidence for cross-generator detection. Comprehensive experiments on images from 23 generative models show that SA-AFL improves ACC by 6.84\% and mAP by 8.25\% over state-of-the-art methods.

Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering properties---such as visual realism and coherent object structure---that matching-based training is intended to learn from the data itself. We argue that this reflects a structural mismatch. Matching losses measure $\ell_2$ regression error on the velocity or score field under training-time marginals, a proxy poorly aligned with the visual and semantic properties that determine sample quality at inference. Given a reward aligned with these properties, RL sidesteps the mismatch by evaluating the model on its own samples and following the reward landscape directly. The challenge is to obtain such a reward without relying on human references, which are expensive and conflate data realism with annotator inclinations. We propose Discriminator-Guided RL (DRL). DRL trains a discriminator to separate data from base-model samples in a pretrained representation space and uses its logit as the reward in KL-regularized RL. The pretrained space restricts the discriminator to perceptually meaningful directions, and the logit estimates the log-likelihood ratio between data and model, which is the optimal reward for targeting the data distribution. Across SiT, JiT, REPA, and RAE, DRL reduces guidance-free FID (e.g., $9.38 \to 2.62$ on SiT) and semantic-space FD (e.g., $88.2 \to 19.3$ on DINOv3 for SiT), with consistent gains across all backbones, and improves human-preference rewards without training on them. It also yields a better Pareto frontier between preference reward and image fidelity under subsequent preference-based post-training, increasing alignment while reducing low-level artifacts such as oversaturation and excessive brightness.

Existing 3D Gaussian Splatting (3DGS) segmentation methods produce accurate rendered masks yet often fail to select a complete set of object Gaussians: removing the predicted Gaussians leaves the rendered depth in the target region nearly unchanged. We trace this gap to the contribution-weighted supervision path of rendering-based feature learning. Because Gaussian geometry and opacity are pre-optimized for RGB reconstruction, rendered-feature losses deliver strong gradients only to high-contribution Gaussians, leaving other Gaussians in the same object region weakly supervised and ultimately unselected. We propose VODA, a plug-and-play supervision objective that addresses this bias. After RGB optimization, Gaussians of the same object portion concentrate at similar depths along the camera ray. A depth-aware rasterizer exploits this property, grouping per-pixel contributing Gaussians into depth-coherent clusters and selecting the cluster that best explains each object pixel. The rendered feature of the selected cluster is then applied as a target directly to every Gaussian within it, bypassing the contribution-weighted gradient path. A complementary pixel-level loss further sharpens boundaries between adjacent objects. To evaluate volumetric completeness, we introduce Removal-3D and the Removal Depth Error (RDE), which measure whether removing the predicted Gaussians induces the expected depth change in 3D. Across four rendering-based baselines, VODA consistently improves volumetric segmentation on Removal-3D while maintaining or improving standard performance on LERF and ScanNet.