3321 papers
This paper introduces ADAS-TO, the first large-scale naturalistic multimodal dataset of 15,659 ADAS-to-manual takeover events from 327 drivers, which combines kinematic and vision-language analysis to characterize safety-critical scenarios and demonstrate that actionable visual cues often precede takeovers by over three seconds.
MipSLAM is a novel 3D Gaussian Splatting SLAM framework that achieves high-fidelity anti-aliased rendering and robust pose estimation by integrating an Elliptical Adaptive Anti-aliasing algorithm, a Spectral-Aware Pose Graph Optimization module, and a local frequency-domain perceptual loss to overcome aliasing artifacts and trajectory drift.
AdaGen introduces a general, learnable framework that employs reinforcement learning with an adversarial reward to dynamically adapt step-specific parameters during iterative image synthesis, thereby overcoming the limitations of static, manually-designed schedules and achieving superior performance across diverse generative models with reduced inference costs.
TrajPred is a novel framework that enhances surgical instrument-tissue interaction recognition in vision-language models by encoding instrument trajectories to capture temporal motion cues and generating fine-grained visual semantic embeddings, thereby significantly improving performance and vision-text alignment on the CholecT50 benchmark.
This paper presents OV-DEIM, a real-time end-to-end DETR-style open-vocabulary object detector that combines the DEIMv2 framework with a query supplement strategy and a novel GridSynthetic data augmentation technique to achieve state-of-the-art performance and efficiency, particularly for rare categories.
This paper introduces TFM, a temporal attack framework that exploits the vulnerability of text-to-video models to generate harmful content by providing only sparse boundary conditions (start and end frames) and implicitly substituting sensitive cues, thereby bypassing existing safety filters and significantly increasing jailbreak success rates.
This paper proposes a novel fine-grained 3D facial reconstruction method for micro-expressions that integrates global dynamic features with locally-enriched cues from 2D motions, facial priors, and 3D geometry to overcome data scarcity and achieve superior geometric accuracy and perceptual detail compared to state-of-the-art approaches.
This paper proposes CAPL, a framework that mitigates multi-image hallucinations in large vision-language models by introducing a selectable image token interaction mechanism for fine-grained cross-image alignment and a preference learning strategy that trains the model to rely on genuine visual evidence rather than textual priors.
SODA introduces a sensitivity-oriented dynamic acceleration framework for Diffusion Transformers that adaptively optimizes caching and pruning strategies through fine-grained sensitivity modeling and dynamic programming, achieving state-of-the-art generation fidelity under controllable acceleration ratios.
MedSteer is a training-free activation-steering framework that generates structurally preserved counterfactual endoscopic images by manipulating cross-attention activations in diffusion transformers, outperforming existing methods in concept editing and downstream medical detection tasks.
This paper introduces VirtueBench, a new benchmark designed to evaluate the trustworthiness of Vision-Language Models in long video understanding by distinguishing between answerable and unanswerable cases to prevent misleading accuracy scores caused by guessing under uncertainty.
This paper introduces PhyVLM-CR, a novel unified framework that integrates Vision-Language Model semantic priors with physical scattering parameters to seamlessly remove both thin and thick clouds from optical remote sensing imagery without explicit cloud-type segmentation, thereby achieving high-fidelity, hallucination-free surface reconstruction.
This paper proposes PSG-UIENet, a novel underwater image enhancement network that integrates Retinex-based illumination correction with CLIP-derived textual semantics to overcome the limitations of existing methods, supported by the introduction of a new large-scale image-text dataset (LUIQD-TD) and a specialized semantic similarity loss function.
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.