2788 papers
This paper introduces a novel framework for EEG-based visual decoding that aligns brain signals with intermediate visual layers via a proposed "Neural Visibility" concept and a Hierarchically Complementary Fusion mechanism, achieving state-of-the-art performance by significantly reducing cross-modal information mismatch.
The paper introduces mAVE, a novel watermarking framework that cryptographically binds audio and video latents in joint generation models to eliminate the "Binding Vulnerability" of existing methods and robustly defend against adversarial Swap Attacks without requiring model fine-tuning.
This paper proposes a facial expression generation method for natural dyadic interaction that leverages human feedback within a vision-language-action framework and reinforcement learning strategy to produce contextually appropriate, identity-independent expressions aligned with human preferences.
NuNext reframes nucleus detection in histopathology as a next-point prediction task using a multimodal large language model trained with spatial-aware soft supervision and reinforcement fine-tuning to achieve superior performance across nine benchmarks.
This paper introduces Semantic-Partitioned Contrastive Learning (S-PCL), a streamlined self-supervised pre-training framework for Chest X-rays that achieves superior accuracy and computational efficiency by enforcing agreement between randomly partitioned semantic subsets, thereby eliminating the need for heavy augmentations, auxiliary decoders, or momentum encoders.
This paper introduces TIQA, a human-aligned text quality assessment task and dataset for generated images, along with the ANTIQA method that significantly outperforms existing OCR and VLM-based metrics in predicting text rendering fidelity and improving downstream generation selection.
This paper proposes Inter-image Pixel Shuffling (IPS), a novel multi-focus image fusion method that synthesizes training data by shuffling pixels between clear and low-pass filtered images to enable deep learning models to learn fusion without real multi-focus datasets, while utilizing a hybrid cross-image network combining CNNs and state space models to achieve superior fusion quality.
This paper introduces EyExIn, a data-efficient framework that enhances retinal Vision Language Models by employing a dual-stream encoding strategy and a deep expert injection mechanism to bridge perception and reasoning gaps, thereby achieving state-of-the-art precision in ophthalmic diagnosis while preventing hallucinations.
The paper introduces AutoSelect, a training-free token pruning method for vision-language models that reformulates token selection as capacity-constrained communication using a noise-gating mechanism to identify and retain only the most informative visual tokens, thereby significantly accelerating inference while preserving nearly all model accuracy.
The paper proposes PDD, a novel framework that unifies global contextual and local structural priors from dual frozen encoders into a shared manifold to distill diverse knowledge into complementary student networks, achieving state-of-the-art performance in medical image anomaly detection across multiple datasets.
The paper introduces CanoVerse, a massive dataset of 320K canonicalized 3D objects and a high-throughput framework that resolves directional ambiguity to significantly improve 3D generation stability, cross-modal retrieval, and zero-shot orientation estimation.
This paper introduces LiveWorld, a novel framework that addresses the "out-of-sight dynamics" limitation in generative video world models by maintaining a persistent global state where unobserved entities continue to evolve, thereby enabling truly continuous 4D world simulation and long-term scene consistency.
PromptGate is a dynamic, federated vision-language framework that utilizes adaptive, learnable prompts to effectively filter out-of-distribution noise from unlabeled medical data pools, thereby significantly improving the efficiency and privacy of open-set active learning across resource-constrained institutions.
The paper proposes ACD-U, an asymmetric co-teaching framework that combines a CLIP-pretrained Vision Transformer with a CNN and incorporates machine unlearning to actively correct selection errors and achieve state-of-the-art robustness against noisy labels.
This paper introduces a framework to evaluate class visualizations and activation atlases for transformer-based pathology models, revealing that while these feature visualization methods effectively capture coarse tissue-level concepts, their ability to represent fine-grained cancer subclasses is limited by intrinsic pathological complexity and reduced inter-observer agreement.
The paper introduces FreeFly-Thinking, an end-to-end Vision-Language Navigation framework for UAVs that leverages a two-stage training strategy and explicit chain-of-thought reasoning to achieve robust and efficient navigation in complex outdoor urban environments.
FastSTAR is a training-free acceleration framework for Spacetime Autoregressive (STAR) video generation that utilizes a novel Spatiotemporal Token Pruning strategy to identify and skip redundant computations, achieving up to a 2.01x speedup with minimal quality degradation.
This paper proposes Shaping Parameter Contribution Patterns (SPCP), a training-time method that enhances out-of-distribution detection by encouraging classifiers to adopt dense, boundary-oriented parameter contribution patterns instead of relying on sparse, brittle ones that lead to overconfident predictions on anomalous inputs.
VINO is a self-supervised learning framework that overcomes the "co-occurrence trap" in dense video by using a teacher-student distillation approach with structural priors to force representations to focus on foreground objects rather than background context, achieving state-of-the-art unsupervised object discovery performance.
LightMedSeg is a lightweight, modular 3D medical image segmentation architecture that leverages anatomical priors, adaptive context modeling, and computational efficiency techniques to achieve high accuracy with minimal parameters and FLOPs, making it a deployable solution for resource-constrained clinical environments.