5332 papers
This paper introduces MOO, a large-scale synthetic multi-view cattle ReID dataset with precise angular annotations, which enables the identification of critical elevation thresholds for improved cross-view generalization and demonstrates significant performance gains when applied to real-world agricultural scenarios.
The paper introduces SPRINT, the first semi-supervised prototypical framework for few-shot class-incremental learning on tabular data, which leverages abundant unlabeled data and low storage costs to achieve state-of-the-art performance across diverse real-world domains.
This paper presents a scalable framework using vision-language models and distributional analysis to evaluate synthetic environmental augmentations, demonstrating that generative AI significantly outperforms rule-based methods in creating realistic adverse-condition images for AI system testing.
Pointer-CAD is a novel LLM-based framework that unifies B-rep models and command sequences through pointer-based entity selection, effectively overcoming the limitations of traditional command-only methods by enabling complex geometric editing and significantly reducing topological errors caused by quantization.
ArtHOI is a novel zero-shot framework that synthesizes physically plausible articulated human-object interactions by formulating the task as a 4D reconstruction problem from monocular video priors, utilizing flow-based segmentation and a decoupled optimization pipeline to overcome the limitations of existing rigid-object methods.
This study benchmarks Denoising Diffusion Probabilistic Models, Latent Diffusion Models, and Flow Matching for synthetic cardiac MRI generation, revealing that DDPM offers the optimal balance of image fidelity, segmentation utility, and privacy preservation under data-scarce conditions.
The paper introduces Hold-One-Shot-Out (HOSO), a validation-free method that learns the blending ratio for CLIP-Adapter using a single hold-out sample, significantly outperforming existing baselines across multiple few-shot datasets without requiring test-set ablation or additional validation sets.
This study demonstrates that augmenting limited real artwork datasets with synthetic images generated via DreamBooth fine-tuning of Stable Diffusion significantly improves the accuracy and generalization of computational authorship attribution models.
This paper proposes a highly data-efficient, one-shot method that leverages a Large Language Model to generate counterfactual descriptions and engineer similarity-based features, enabling the accurate prediction of Vision-Language Foundation Models' zero-shot performance on underrepresented domains without requiring large labeled test sets.
The paper proposes RANGER, a novel framework for pathology report generation that combines a sparsely-gated Mixture-of-Experts decoder with an adaptive retrieval re-ranking module to overcome limitations in existing transformer-based approaches by enabling dynamic expert specialization and reducing noise from external knowledge retrieval.
FocusGraph is a novel framework for embodied long video question answering that combines a lightweight Scene-Caption LLM Selector for identifying query-relevant clips and a training-free Patch-wise Sparse-Flow Retention method for keyframe selection, achieving state-of-the-art performance on egocentric benchmarks while significantly reducing inference time.
Helios is a pioneering 14B autoregressive diffusion model that achieves real-time, minute-scale video generation with high robustness and quality on a single GPU, eliminating the need for common anti-drifting heuristics, standard acceleration techniques, or parallelism frameworks through novel training strategies and infrastructure optimizations.
TaxonRL is a reinforcement learning framework that employs hierarchical intermediate rewards to decompose fine-grained visual reasoning into structured taxonomic steps, achieving state-of-the-art accuracy and interpretable decision-making that surpasses human performance on challenging species classification tasks.
The paper introduces ZipMap, a stateful feed-forward model that utilizes test-time training to achieve linear-time, bidirectional 3D reconstruction with accuracy matching quadratic-time methods while offering over 20x faster inference speeds for large image collections.
The paper proposes SimpliHuMoN, a versatile transformer-based model that unifies trajectory and pose prediction into a single end-to-end framework, achieving state-of-the-art results across multiple benchmarks without requiring task-specific modifications.
This paper proposes "Thought Flow Nets," a self-correcting mechanism inspired by Hegelian dialectics that enables AI models to iteratively refine predictions through a sequence of thoughts, thereby improving both model accuracy and human user performance compared to single-output approaches.
This paper introduces VQA-MHUG, a novel multimodal gaze dataset, and demonstrates that for the first time, higher correlation between neural and human attention on text is a significant predictor of Visual Question Answering model performance, highlighting the need for improved text attention mechanisms in vision-language tasks.
The paper introduces MULAN, a novel Visual Question Answering model that integrates human-like attention from both image and text modalities into neural self-attention layers, achieving state-of-the-art performance on the VQAv2 dataset with significantly fewer trainable parameters than prior methods.
This paper demonstrates that off-the-shelf vision foundation models, particularly the weakly-supervised BiomedCLIP, can serve as highly effective, general-purpose feature extractors for content-based image retrieval in radiology, achieving performance comparable to specialized systems across 1.6 million images and 161 pathologies without requiring additional training.
The paper introduces "Implicit-Zoo," a large-scale dataset of neural implicit functions spanning diverse 2D and 3D scenes, designed to overcome resource and data limitations while demonstrating improved performance in image classification, semantic segmentation, and 3D pose regression.