5412 papers
This paper introduces the Garbage Dataset (GD), a publicly available benchmark of 12,259 labeled images across 10 waste categories, and evaluates its effectiveness using state-of-the-art deep learning models to advance automated waste segregation while addressing challenges like class imbalance, background complexity, and environmental trade-offs.
The paper introduces EO-VAE, a unified multi-sensor variational autoencoder that employs dynamic hypernetworks to efficiently tokenize diverse Earth observation data with flexible channel combinations, achieving superior reconstruction fidelity compared to existing modality-specific tokenizers.
MedXIAOHE is a state-of-the-art medical vision-language foundation model that leverages an entity-aware continual pretraining framework, reinforcement learning, and tool-augmented agentic training to achieve superior diagnostic reasoning, reliability, and performance across diverse medical benchmarks.
This paper introduces UniTAF, a modular framework that unifies Text-to-Speech and Audio-to-Face models to enable internal feature transfer and emotion control, validating the feasibility of reusing intermediate representations for improved audio-facial consistency without prioritizing generation quality.
CRAFT-LoRA is a novel framework that achieves high-fidelity, personalized image generation with improved content-style disentanglement and flexible semantic control by combining rank-constrained fine-tuning, prompt-guided expert aggregation, and a training-free timestep-dependent guidance scheme, all without requiring additional retraining overhead.
This paper introduces CFE-Bench, a challenging multimodal benchmark derived from authentic university exam problems across 20+ STEM domains, which reveals that even frontier models struggle with maintaining correct intermediate states and step efficiency in multi-step reasoning despite achieving moderate overall accuracy.
This paper introduces TraqPoint, a novel end-to-end reinforcement learning framework that reframes keypoint detection as a sequential decision-making problem to directly optimize trackability across image sequences, significantly outperforming existing state-of-the-art methods in 3D vision tasks.
This paper introduces Concept Distillation Sampling (CDS), a novel training-free framework that enables high-fidelity, multi-concept image editing by integrating a stable distillation backbone with dynamic weighting and spatially-aware LoRA priors to overcome the limitations of text-based optimization methods.
Uni-Animator is a novel Diffusion Transformer-based framework that unifies image and video sketch colorization by introducing instance patch embedding for precise color transfer, physical feature reinforcement for high-frequency detail preservation, and sketch-based dynamic RoPE encoding to ensure temporal coherence in large-motion scenes.
This paper introduces MedMAP, a modality-aware pre-training framework and the accompanying MedMoM-MRI3D dataset, which collectively enhance vision-language representation learning to significantly improve 3D MRI multi-organ abnormality detection across twelve modalities.
The paper proposes APPO, an attention-guided policy optimization algorithm that leverages token-level dense rewards to enhance fine-grained video perception through reasoning, demonstrating that improving perception yields greater performance gains than scaling reasoning capabilities alone.
This paper presents a novel approach to automating the curation and retrieval of 16th and 17th-century shipbuilding treatises by leveraging generative AI models like SAM2, Florence2, and ChatGPT, enhanced with specialized nautical ontologies, to overcome the challenges of segmenting and labeling scarce, domain-specific historical documents.
This paper presents Lilium, an automated evolutionary method that enhances the accuracy and robustness of craniofacial superimposition by modeling soft-tissue variability with a 3D cone-based representation optimized via Differential Evolution while enforcing multiple anatomical and photographic constraints.
GLIDE-Reg is a robust deformable registration method that jointly optimizes a registration field with a learnable dimensionality reduction module to fuse global semantic cues from foundation models with local handcrafted descriptors, achieving state-of-the-art performance in anatomical alignment and nodule tracking across diverse medical imaging cohorts.
This paper proposes IDER, a novel continual learning framework that leverages an idempotence distillation loss to seamlessly integrate uncertainty awareness with existing replay methods, thereby effectively reducing catastrophic forgetting while improving prediction reliability and accuracy.
The paper introduces BornoViT, a novel and highly efficient lightweight Vision Transformer model with only 0.65 million parameters that achieves 95.77% accuracy on the BanglaLekha dataset for classifying Bengali handwritten characters and digits, offering a resource-friendly alternative to computationally expensive state-of-the-art models.
ShiftLUT is an efficient image restoration framework that achieves a significantly larger receptive field and improved performance over state-of-the-art methods by integrating a Learnable Spatial Shift module, an asymmetric dual-branch architecture, and an Error-bounded Adaptive Sampling compression strategy, all while maintaining low storage and inference costs suitable for edge devices.
This paper introduces the Multimodal Weight Predictor (MWP) framework and the Waste-Weight-10K dataset to accurately estimate commercial and industrial waste weight by fusing RGB images with physics-informed metadata through a Vision Transformer and Stacked Mutual Attention, achieving high precision across a wide weight range while providing human-readable explanations via SHAP and large language models.
PreciseCache is a plug-and-play framework that significantly accelerates video generation by employing Low-Frequency Difference-based step-wise caching (LFCache) and block-wise caching (BlockCache) to precisely identify and skip truly redundant computations, thereby achieving substantial speedups without sacrificing output quality.
The paper introduces FlowReg, a flow-matching framework for unsupervised cardiac MR registration that utilizes warmup-reflow training and an Initial Guess strategy to achieve state-of-the-art accuracy with as few as two inference steps, eliminating the need for pre-trained models or segmentation labels.