WildBox provides over 237k 3D wildlife annotations from drone video and benchmarks reveal zero-shot 3D detection at 0 AP but fine-tuned performance of 8.68 AP-BEV and 13.17 AP3D, with depth estimation causing most errors.
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In: Proceedings of the IEEE/CVF Conference on Computer 25 Vision and Pattern Recognition, pp
Canonical reference. 71% of citing Pith papers cite this work as background.
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Mind2Web is the first large-scale dataset of real-world web tasks for developing generalist language-guided agents that complete complex actions on diverse websites.
MLLMs drop from over 85% accuracy on action presence to under 50% on matched action-denial videos, exposing a causal verification gap that causal graph prompts partially close.
A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
HASTE enables training-free dynamic compression of pre-trained CNNs by patch-wise LSH-based merging of redundant channels, reporting 46.2% FLOPs reduction on ResNet34 CIFAR-10 with 1.25% accuracy drop.
AirGroundBench is a new diagnostic benchmark exposing that MLLMs handle basic spatial perception but struggle with cross-view alignment, transformation reasoning, and embodied navigation under heterogeneous air-ground views.
ScaLe-INR is a multi-branch INR architecture that applies directional scaling per the Fourier inverse theorem and a directional edge guidance loss to disentangle scales and improve reconstruction fidelity.
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
The paper defines the 4DVLT task for worldline-centered 4D scene understanding, releases Instruct-4D with 129.4K QA pairs, and presents 4DTrack achieving 62.68 TGA_Top1, outperforming adapted baselines by 19.62 points.
DUNE enables exact data-consistency gradients via VJP when deep unrolled networks operate in representation space, yielding better MRI reconstructions than prior heuristic-DC variants.
REST-TS resolves text collapse in multimodal time series forecasting by exclusively supervising the text branch on numerical residuals to compel genuine content extraction from text descriptions.
HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
iSAGE achieves near-dense mIoU performance in remote sensing semantic segmentation using iterative expert clicks on confident model errors with an error-weighted loss, using only 0.011-0.04% of pixels.
World models introduce a stealthy poisoning vector into robot learning pipelines where malicious prompts or dynamics in teleoperated data activate only during synthetic trajectory generation, enabling backdoors in downstream policies.
Attributed Feature Graphs (AFGs) represent CAD features as attributed nodes and relations as directed edges to enable GNN surrogate models that predict design performance with feature-level interpretability on the CarHoods10K dataset.
LL-Bench supplies a human-annotated dataset exposing generative model weaknesses in low-level restoration and introduces LL-Score as an MLLM evaluator that outperforms existing quality metrics and can serve as a training reward.
DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
DELOS applies contrastive learning to phase-folded light curves to detect shallow intermediate-to-long period transits, reporting 15.5% and 11.25% gains in combined precision-recall over BLS and TLS in low-SNR tests plus 3-80x speedups.
Introduces Abstraction Gap metric and CAGE benchmark showing seven of eight VLMs have large gaps between text plausibility and chain-based causal reasoning, with one model succeeding.
Morpheus learns morphable category-level shape priors to produce implicit 3D correspondences in camera space without explicit supervision and releases the HouseCorr3D benchmark with amodal and symmetry annotations.
ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
RS2AD-LiDAR reconstructs vehicle LiDAR data from roadside observations via coordinate transformation, virtual LiDAR modeling and resampling, claimed as the first such method, with experiments showing improved object detection when mixed with real data.
Introduces NMCA-aligned L1/L2 LULC schemes and the Loosdorf-MSL benchmark dataset, with Point Transformer V3 reaching 79.4% mIoU on 8 classes and 58.9% on 20 classes, plus gains from multispectral inputs.
CelloCut formulates watertight remeshing as binary labeling on a Delaunay tetrahedral partition solved by graph-cut minimization with one-sided constraints to guarantee volumetrically consistent solids.
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WildBox: A Dataset and Benchmark for Aerial Monocular 3D Detection of African Savanna Wildlife
WildBox provides over 237k 3D wildlife annotations from drone video and benchmarks reveal zero-shot 3D detection at 0 AP but fine-tuned performance of 8.68 AP-BEV and 13.17 AP3D, with depth estimation causing most errors.
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Mind2Web: Towards a Generalist Agent for the Web
Mind2Web is the first large-scale dataset of real-world web tasks for developing generalist language-guided agents that complete complex actions on diverse websites.
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Learning to Deny: Action Denial in Multimodal Large Language Models
MLLMs drop from over 85% accuracy on action presence to under 50% on matched action-denial videos, exposing a causal verification gap that causal graph prompts partially close.
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Diffusion-Based Material Regularization for Physics-Based Inverse Rendering
A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
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HASTE: A Framework for Training-Free, Dynamic, and Steerable Compression of Pre-Trained Convolutional Neural Networks
HASTE enables training-free dynamic compression of pre-trained CNNs by patch-wise LSH-based merging of redundant channels, reporting 46.2% FLOPs reduction on ResNet34 CIFAR-10 with 1.25% accuracy drop.
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AirGroundBench: Probing Spatial Intelligence in Multimodal Large Models under Heterogeneous Multi-View Embodied Collaboration
AirGroundBench is a new diagnostic benchmark exposing that MLLMs handle basic spatial perception but struggle with cross-view alignment, transformation reasoning, and embodied navigation under heterogeneous air-ground views.
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ScaLe-INR: Scale and Learn Implicit Neural Representations
ScaLe-INR is a multi-branch INR architecture that applies directional scaling per the Fourier inverse theorem and a directional edge guidance loss to disentangle scales and improve reconstruction fidelity.
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Semantic Browsing: Controllable Diversity for Image Generation
A technique for controllable diversity in text-to-image generation by inducing structured semantic variations at the prompt level via VLM and agentic workflow.
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4DVLT: Dynamic Scene Understanding with Worldline-Centered Vision-Language Tracking
The paper defines the 4DVLT task for worldline-centered 4D scene understanding, releases Instruct-4D with 129.4K QA pairs, and presents 4DTrack achieving 62.68 TGA_Top1, outperforming adapted baselines by 19.62 points.
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Deep Unrolled Networks in Representation Space Applied to MRI Reconstruction
DUNE enables exact data-consistency gradients via VJP when deep unrolled networks operate in representation space, yielding better MRI reconstructions than prior heuristic-DC variants.
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Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
REST-TS resolves text collapse in multimodal time series forecasting by exclusively supervising the text branch on numerical residuals to compel genuine content extraction from text descriptions.
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Human Universal Grasping
HUG trains a flow-matching model on a new 1M-frame egocentric human grasp dataset to generate retargetable grasps from single RGB-D images, beating baselines by 23-34% on a new 90-object benchmark.
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iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision
iSAGE achieves near-dense mIoU performance in remote sensing semantic segmentation using iterative expert clicks on confident model errors with an error-weighted loss, using only 0.011-0.04% of pixels.
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Targeting World Models to Compromise Robot Learning Pipelines
World models introduce a stealthy poisoning vector into robot learning pipelines where malicious prompts or dynamics in teleoperated data activate only during synthetic trajectory generation, enabling backdoors in downstream policies.
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Bridging CAD and Data-Driven Design: Attributed Feature Graphs for Engineering Design
Attributed Feature Graphs (AFGs) represent CAD features as attributed nodes and relations as directed edges to enable GNN surrogate models that predict design performance with feature-level interpretability on the CarHoods10K dataset.
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LL-Bench: Rethinking Low-Level Vision Evaluation in the Era of Large-Scale Generative Models
LL-Bench supplies a human-annotated dataset exposing generative model weaknesses in low-level restoration and introduces LL-Score as an MLLM evaluator that outperforms existing quality metrics and can serve as a training reward.
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DPA4: Pushing the Accuracy-Cost Frontier of Interatomic Potentials with EMFA SO(2) Convolution
DPA4 is a new SE(3)-equivariant interatomic potential with EMFA SO(2) convolution that sets new accuracy-cost records on Matbench Discovery and SPICE benchmarks using fewer parameters than prior models.
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DELOS: Detecting Shallow Transits in Kepler Photometry Using a Contrastive-Learning Framework
DELOS applies contrastive learning to phase-folded light curves to detect shallow intermediate-to-long period transits, reporting 15.5% and 11.25% gains in combined precision-recall over BLS and TLS in low-SNR tests plus 3-80x speedups.
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The Abstraction Gap in Vision-Language Causal Reasoning
Introduces Abstraction Gap metric and CAGE benchmark showing seven of eight VLMs have large gaps between text plausibility and chain-based causal reasoning, with one model succeeding.
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Category-Level 3D Correspondence in Camera Space via Morphable Object Priors
Morpheus learns morphable category-level shape priors to produce implicit 3D correspondences in camera space without explicit supervision and releases the HouseCorr3D benchmark with amodal and symmetry annotations.
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ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation
ClothTransformer is a unified latent-space Transformer for cloth simulation that handles body-driven garments, robotic manipulation, and free-fall collisions in one model with 4-9x lower error than prior methods and mesh-resolution independence.
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RS2AD-LiDAR: End-to-End Autonomous Driving LiDAR Data Generation from Roadside Sensor Observations
RS2AD-LiDAR reconstructs vehicle LiDAR data from roadside observations via coordinate transformation, virtual LiDAR modeling and resampling, claimed as the first such method, with experiments showing improved object detection when mixed with real data.
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3D LULC classification using multispectral LiDAR and deep learning: current and prospective schemes
Introduces NMCA-aligned L1/L2 LULC schemes and the Loosdorf-MSL benchmark dataset, with Point Transformer V3 reaching 79.4% mIoU on 8 classes and 58.9% on 20 classes, plus gains from multispectral inputs.
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CelloCut: Constructive Watertight Remeshing via Tetrahedral Cell Cuts
CelloCut formulates watertight remeshing as binary labeling on a Delaunay tetrahedral partition solved by graph-cut minimization with one-sided constraints to guarantee volumetrically consistent solids.
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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
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MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
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TokenRatio: Principled Token-Level Preference Optimization via Ratio Matching
Introduces TBPO, which derives a Bregman-divergence density-ratio matching objective for token-level preference optimization that generalizes DPO while preserving the induced optimal policy.
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Geometrically Approximated Modeling for Emitter-Centric Ray-Triangle Filtering in Arbitrarily Dynamic LiDAR Simulation
GRCA uses emitter-centric geometric culling of rays per triangle to accelerate LiDAR simulation in arbitrarily dynamic scenes, reporting up to 14.55x speedup over Embree and 7.97x over OptiX.
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AnomalyClaw: A Universal Visual Anomaly Detection Agent via Tool-Grounded Refutation
AnomalyClaw turns single-step VLM anomaly judgments into a multi-round tool-grounded refutation process, delivering consistent macro-AUROC gains of 3.5-7.9 percentage points over direct inference across 12 cross-domain datasets.
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LLM-guided Semi-Supervised Approaches for Social Media Crisis Data Classification
LG-CoTrain, an LLM-guided co-training method, outperforms classical semi-supervised baselines for crisis tweet classification in low-resource settings with 5-25 labeled examples per class.
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Hyperbolic Concept Bottleneck Models
HypCBM reformulates concept activations as geometric containment in hyperbolic space to produce sparse, hierarchy-aware signals that match Euclidean models trained on 20 times more data.
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Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration
CEA assembles per-token low-rank residual updates via dense affinities over hyper-adapter-generated components to improve all-in-one image restoration on spatially non-uniform degradations.
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Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
EstGraph benchmark evaluates LLMs on estimating properties of very large graphs from random-walk samples that fit in context limits.
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Congestion-Aware Dynamic Axonal Delay for Spiking Neural Networks
CADAD adds activity-dependent dynamic delays to SNNs, improving accuracy on speech datasets while cutting parameter count by about 50% versus prior static delay approaches.
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TRIP-Evaluate: An Open Multimodal Benchmark for Evaluating Large Models in Transportation
TRIP-Evaluate is a new open multimodal benchmark with 837 text, image, and point-cloud items organized by a role-task-knowledge taxonomy to evaluate large models on transportation workflows.
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Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning
Trust-SSL introduces additive-residual trust weights in SSL to selectively handle corruptions in aerial imagery, yielding higher linear-probe accuracy and larger gains under severe degradations than SimCLR or VICReg.
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Divide-and-Conquer Approach to Holistic Cognition in High-Similarity Contexts with Limited Data
DHCNet improves ultra-fine-grained visual categorization by progressively building holistic cognition from local discrepancies using self-shuffling and refinement on limited data.
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PokeGym: A Visually-Driven Long-Horizon Benchmark for Vision-Language Models
PokeGym is a new benchmark that tests VLMs on long-horizon tasks in a complex 3D game using only visual observations, identifying deadlock recovery as the primary failure mode.
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A global dataset of continuous urban dashcam driving
CROWD is a new global dataset of 51,753 continuous urban dashcam segments spanning over 20,000 hours from 238 countries, with manual labels and automated object detections for routine driving analysis.
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DuFal: Dual-Frequency-Aware Learning for High-Fidelity Extremely Sparse-view CBCT Reconstruction
DuFal combines global and local high-frequency Fourier neural operators with cross-attention fusion to recover fine anatomical structures in extremely sparse-view CBCT, outperforming prior methods on LUNA16 and ToothFairy data.
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GLUE: Coordinating Pre-Trained Generative Models for System-Level Design
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
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BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations
BEVCALIB performs LiDAR-camera calibration from raw data by fusing camera and LiDAR bird's-eye view features with a novel feature selector and reports state-of-the-art accuracy on KITTI and NuScenes.
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OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotations
OOD-SEG reframes multi-class segmentation from sparse positive-only annotations as pixel-wise positive-unlabelled learning solved by integrating out-of-distribution detection techniques, with a proposed cross-validation evaluation on surgical imaging datasets.
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Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
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FLORA: A deep learning approach to predict forest attributes from heterogeneous LiDAR data
FLORA is an octree-based deep learning framework with auxiliary data fusion that predicts forest attributes from heterogeneous LiDAR, achieving rRMSE of 12.3% for dominant height and 39% for total volume on 32k French NFI plots.
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PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
PGUDA uses pressure signals to train a teacher network that distills modality-invariant knowledge into an sEMG student via cross-modal distillation, reaching 58.08% cross-subject accuracy with only 5% labeled data for the teacher.
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Fleet: Few Shots Lead Effective AI-generated Image Detection
Fleet achieves dynamic few-shot adaptation for AIGI detection via avoidance routing in decoupled subspaces, raising accuracy from 20.4% to 73.1% on new generators like Doubao Seedream 4.0 with 10 shots.
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RBE-Flow: Recurrent Bayesian Estimation on Feature Manifolds for Cross-Modal Registration
RBE-Flow recasts dense cross-modal flow estimation as closed-loop recurrent Bayesian estimation on learned feature manifolds with uncertainty-adaptive updates and achieves SOTA on three registration benchmarks.
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Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
A physics-informed self-supervised framework learns detector calibration parameters and ionic charge-state predictions jointly from raw spectrometer data using iterative pseudo-labelling driven by physical constraints.
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Confidence-feedback-weighted graph matching network: online-offline laser-induced damage site matching under complex interference
A confidence-feedback-weighted graph matching network achieves 96.36% F1-score on damage site matching by using matchability confidence to weight edge features and applying geometric consistency and hard-example mining.