5334 papers
The paper introduces Waffle, a novel fine-tuning strategy that employs structure-aware attention and contrastive learning to significantly enhance multi-modal models' ability to convert UI designs into functional HTML code, outperforming existing methods on both new and established benchmarks.
The paper proposes RealOSR, a diffusion-based framework for real-world omnidirectional image super-resolution that utilizes a novel Latent Gradient Alignment Routing (LaGAR) module to enable efficient one-step denoising, achieving significant visual quality improvements and over 200 inference acceleration compared to existing methods.
The paper presents Slot-BERT, a bidirectional long-range self-supervised model that overcomes the temporal coherence and computational limitations of existing methods to achieve robust, scalable object discovery and zero-shot domain adaptation in long surgical videos.
This paper introduces the first framework capable of learning architecture-agnostic representations for diverse Neural Radiance Fields (NeRFs) by training a Graph Meta-Network with a contrastive objective, enabling robust inference across multiple and unseen NeRF architectures for tasks like classification and retrieval.
This paper introduces a novel, fully unsupervised cycle-consistent multi-graph matching approach that achieves state-of-the-art accuracy in semantic cell annotation for *C. elegans* 3D microscopy images, enabling the creation of the first unsupervised cell atlas without requiring ground truth labels.
This paper proposes a computationally efficient, model-agnostic, single-stage GAN-based defense strategy that significantly improves the robustness and accuracy of traffic sign classification in autonomous vehicles against adversarial patch attacks without requiring prior knowledge of the patch design.
This paper introduces LaGoVAD, a novel language-guided open-world video anomaly detection framework that dynamically adapts to variable anomaly definitions via natural language prompts under weak supervision, supported by the newly proposed PreVAD dataset and validated by state-of-the-art zero-shot performance across seven benchmarks.
This paper introduces SwD, a scale-wise diffusion distillation framework that combines a progressive generation strategy with a novel Maximum Mean Discrepancy-based patch-level objective to significantly accelerate sampling in large-scale text-to-image and video models while outperforming existing methods under the same compute budget.
This paper introduces the first comprehensive framework for differentially private 2D human pose estimation that combines Projected DP-SGD and Feature Differential Privacy to effectively balance formal privacy guarantees with high model accuracy, achieving a mean PCKh@0.5 of 82.61% at on the MPII dataset.
The paper proposes ANSE, a model-aware framework that leverages a Bayesian attention-based uncertainty metric (BANSA) to automatically select optimal initial noise seeds for video diffusion models, thereby improving generation quality and temporal coherence with minimal inference overhead.
This paper introduces SABER, a novel framework that generates spatially consistent, universal 3D adversarial objects to realistically and effectively attack Bird's-Eye-View detectors in autonomous driving by optimizing non-invasive environmental manipulations that maintain multi-view and temporal consistency.
This paper introduces Interaction Field Matching (IFM), a generalized framework that overcomes the modeling complexities of Electrostatic Field Matching by leveraging a novel interaction field inspired by strong quark-antiquark interactions to improve data generation and transfer performance.
This paper introduces HSSBench, a comprehensive multilingual benchmark featuring over 13,000 samples generated through a novel expert-agent collaboration pipeline, designed to evaluate and address the current limitations of Multimodal Large Language Models in handling the interdisciplinary and abstract reasoning tasks characteristic of the Humanities and Social Sciences.
This paper introduces Frame Guidance, a training-free method that enables fine-grained, frame-level control over video generation in diffusion models through efficient latent processing and optimization, eliminating the need for costly fine-tuning while supporting diverse tasks like keyframe guidance, stylization, and looping.
Perception-R1 addresses the limitation of existing RLVR methods in enhancing multimodal perception by introducing a novel visual perception reward derived from Chain-of-Thought annotations, which effectively boosts both perception and reasoning capabilities of Multimodal Large Language Models to achieve state-of-the-art performance with minimal training data.
StreamSplat is a fully feed-forward framework that enables real-time, online reconstruction of dynamic 3D scenes from uncalibrated video streams into 3D Gaussian Splatting representations, achieving state-of-the-art quality with a 1200x speedup over traditional optimization-based methods through probabilistic sampling, bidirectional deformation, and adaptive Gaussian fusion.
The paper proposes ECAD, a genetic algorithm-based evolutionary caching method that learns optimal, model-specific inference schedules to significantly accelerate off-the-shelf diffusion models while maintaining high image quality and generalizing across resolutions and architectures without requiring parameter modifications.
This paper demonstrates that generating images on-the-fly via Text-to-Image models can unlock latent visual priors to significantly enhance text-centric reasoning, provided there is strong semantic alignment, task visual groundability, and high generative fidelity.
SceneStreamer is a unified autoregressive transformer framework that generates continuous, long-horizon traffic scenarios by predicting sequences of tokens representing dynamic elements like agents and traffic signals, thereby enabling the creation of realistic, diverse, and adaptive environments that significantly improve the robustness and generalization of autonomous driving policies.
This paper proposes a novel, memory-efficient annealing guidance scheduler that dynamically adjusts the guidance scale during diffusion sampling based on conditional noisy signals, thereby significantly improving both image quality and text alignment without requiring additional activations.