4100 papers
NOVA3R is a feed-forward approach for 3D reconstruction from unposed images that employs a scene-token mechanism and diffusion-based decoder to learn a global, view-agnostic representation, thereby overcoming pixel-aligned limitations to produce complete and physically plausible amodal reconstructions.
This paper proposes a unified, morphology-decoupled framework that leverages cross-task attention and mixed-supervision losses to overcome feature interference and class imbalance, achieving state-of-the-art joint detection of lacunes and enlarged perivascular spaces on both the VALDO 2021 and external EPAD cohorts.
The paper introduces "Gaussian Wardrobe," a novel framework that utilizes a compositional 3D Gaussian representation to disentangle shape-agnostic garment layers from human bodies, enabling high-fidelity photorealistic avatars and versatile virtual try-on applications with free-form clothing transfer.
This study demonstrates that introducing "Semantic Anchoring," a text-alignment mechanism, effectively resolves intrinsic embedding collapse and domain-locking in cross-species pathology models by using language as a stable coordinate system to re-align visual features, thereby significantly improving cancer detection performance across same-cancer, cross-cancer, and cross-species scenarios.
This paper introduces "Dual Tuning," a framework that quantifies the performance gains of reasoning versus direct answering to establish a "Thinking Boundary," thereby challenging the universal application of reasoning and providing data-driven guidance for resource-efficient, adaptive multimodal model training.
SkillNet is an open infrastructure that addresses the lack of systematic skill accumulation in AI agents by providing a unified ontology, a repository of over 200,000 skills, and evaluation tools to create, connect, and assess skills, thereby significantly enhancing agent performance and efficiency across diverse tasks.
This paper proposes a multi-modal deep learning framework that fuses 3D CNN-based video features, Graph Convolutional Network-analyzed pose data, and object detection context via cross-attention to robustly recognize daily activities for Ambient Assisted Living, achieving competitive accuracy on the Toyota SmartHome dataset.
This paper introduces InverseNet, the first cross-modality benchmark demonstrating that operator mismatch severely degrades deep learning performance in compressive imaging, while revealing that operator-conditioned architectures and blind calibration can effectively recover these losses across simulated and real-world scenarios.
This study evaluates various deep learning fusion and grouping strategies for classifying Local Climate Zones using multimodal SAR and MSI data, demonstrating that a baseline hybrid fusion model combined with band grouping and label merging achieves the highest accuracy (76.6%) while significantly improving predictions for underrepresented classes.
The paper proposes Dual-LoRA Controllable Diffusion, a unified framework that leverages multi-class nuclei centroids as spatial priors and task-specific LoRA adapters to simultaneously achieve high-fidelity local structure completion and realistic global tissue synthesis, significantly outperforming existing GAN and diffusion baselines in histopathology modeling.
This paper introduces Partial Vision Mamba (PVM), a novel architectural component that adapts State Space Models like Mamba to handle arbitrarily shaped invalid data through mask-aware operations, demonstrating its effectiveness across depth completion, image inpainting, and classification tasks.
The paper introduces PinPoint, a comprehensive real-world benchmark for Composed Image Retrieval featuring multi-answer ground truths, explicit hard negatives, and multi-image queries to reveal significant limitations in current methods, alongside proposing a training-free MLLM-based reranking solution to address these gaps.
The paper introduces SGR3, a training-free 3D scene graph generation framework that leverages multi-modal large language models and retrieval-augmented generation with a weighted patch-level similarity mechanism to achieve competitive performance without requiring explicit 3D reconstruction or multi-modal data.
Spinverse is a differentiable physics framework that reconstructs explicit microstructural interfaces from diffusion MRI by optimizing learnable face permeabilities on a fixed tetrahedral grid, utilizing geometric priors and multi-sequence optimization to overcome ill-posedness and recover complex tissue geometries without altering mesh connectivity.
This study demonstrates that a multimodal approach combining vision and language models (GPT-4.1-nano) to analyze both visualization images and text features significantly outperforms unimodal methods in predicting the difficulty of data literacy test items for U.S. adults, achieving a mean absolute error of 0.224.
This paper proposes sFRC, a novel method that performs Fourier Ring Correlation analysis over small patches to robustly detect and quantify hallucinations in deep learning-based medical image restoration across various undersampled imaging problems.
This paper introduces PulseFocus, a training-free inference-time method that mitigates diffuse attention patterns and positional biases in reasoning VLMs by structuring chain-of-thought generation into interleaved planning and focus blocks, thereby significantly improving performance on multi-image benchmarks.
This paper presents a systematic benchmark study evaluating the effectiveness of pruning, quantization, and knowledge distillation in compressing neural networks for hyperspectral image classification, demonstrating that these methods can significantly reduce model size and computational costs while maintaining competitive accuracy for resource-constrained remote sensing applications.
This paper evaluates the viability of zero-shot Multimodal LLMs for real-world video anomaly detection, revealing that while prompt engineering can significantly improve F1-scores, a persistent conservative bias toward the "normal" class severely limits recall, highlighting a critical gap between current MLLM capabilities and the demands of practical surveillance.
This paper introduces FOZO, a memory-efficient, backpropagation-free test-time adaptation method that utilizes zeroth-order prompt optimization with dynamically decaying perturbations to achieve superior performance on resource-constrained devices and quantized models compared to existing gradient-based and forward-only approaches.