5572 papers
This paper proposes a straightforward and effective Convolutional Neural Network (CNN) scheme for detecting faults in PV solar panels, achieving high accuracy rates of 91.1% for binary classification and 88.6% for multi-classification while outperforming previous models using the same datasets.
This paper addresses reward overoptimization in diffusion models by identifying mismatches with temporal inductive bias and the regularization role of dormant neurons, leading to the proposal of TDPO-R, a policy gradient algorithm that leverages temporal inductive bias and resets active neurons to mitigate primacy bias.
This paper introduces Uni-ISP, a novel pipeline that unifies the learning of image signal processors for diverse mobile cameras using device-aware embeddings and a specialized training scheme, supported by a new real-world 4K dataset called FiveCam, to achieve superior accuracy, adaptability, and versatility in both forward and inverse ISP tasks.
This paper proposes R2GenCSR, a novel radiology report generation framework that leverages the linear-complexity Mamba architecture for efficient visual feature extraction and enhances LLM performance by mining both contextual and residual information from training samples to generate high-quality medical reports.
The paper proposes Shuffle Mamba, a novel multi-modal image fusion framework that employs a Bayesian-inspired Random Shuffle scanning strategy and its inverse to eliminate biases from fixed scanning patterns, thereby achieving robust cross-modality interaction and superior fusion quality through Monte-Carlo averaging.
This paper proposes the first label unlearning method for Vertical Federated Learning that utilizes a representation-level manifold mixup mechanism to generate synthetic embeddings for gradient-based forgetting and recovery, effectively removing sensitive label information while preserving model utility and computational efficiency across diverse datasets.
Stereo-Talker is a novel one-shot audio-driven system that synthesizes high-fidelity, 3D talking videos with precise lip synchronization and expressive gestures by leveraging LLM priors for motion generation and a prior-guided Mixture-of-Experts mechanism for stable, view-controllable video synthesis.
This paper proposes Stepwise Diffusion Policy Optimization (SDPO), a novel reinforcement learning framework that aligns few-step diffusion models with specific objectives by introducing dual-state trajectory sampling and latent similarity-based dense reward prediction to enable low-variance, granular policy updates and superior reward-aligned image synthesis.
TREND introduces the first unsupervised 3D representation learning framework for LiDAR perception that leverages temporal forecasting via a Recurrent Embedding scheme and Temporal Neural Field to predict future observations, achieving significant performance gains over existing methods on downstream 3D object detection tasks.
CLAP introduces an unsupervised joint pre-training framework for 3D perception that leverages curvature sampling to overcome computational bottlenecks and learnable prototypes with an EM-based training scheme to effectively fuse image and point cloud modalities, achieving significant performance gains over state-of-the-art methods.
This paper introduces GenVidBench, the largest dataset to date containing 6.78 million videos from 11 state-of-the-art generators, designed to overcome current limitations in developing and evaluating robust AI-generated video detection models through its large scale, cross-source diversity, and coverage of cutting-edge generation technologies.
This paper proposes a multi-scale deep learning framework that utilizes a tri-branch convolutional network and an attentional fusion module to represent illuminant maps as linear combinations of multi-grained components, thereby achieving state-of-the-art performance in multi-illuminant color constancy by effectively addressing the impact of image scales.
This paper introduces DSV, a system that accelerates large-scale video Diffusion Transformer training by exploiting empirically observed dynamic attention sparsity through a two-stage approximation algorithm and hybrid sparsity-aware context parallelism, achieving up to 3.02x higher throughput without compromising generation quality.
This paper proposes a robust, inference-time watermarking method for generative models that embeds detectable and user-identifiable marks into generated images without retraining, offering provable resistance to additive perturbations and synthetic removal attacks.
The paper proposes JiSAM, a plug-and-play method combining jittering augmentation, a domain-aware backbone, and memory-based sectorized alignment to significantly reduce the labeling burden and address corner case scarcity in autonomous driving by enabling high-performance 3D perception using only 2.5% of real-world labeled data augmented with synthetic data.
This paper introduces ARPG, a novel autoregressive image generation model that employs a decoupled decoding framework to enable randomized parallel inference, thereby achieving significant improvements in speed, memory efficiency, and zero-shot generalization compared to traditional raster-order approaches.
This paper proposes a novel approach for generating realistic, scene-scale 3D semantic data without relying on image projections or decoupled multi-resolution models, demonstrating that the resulting synthetic annotations effectively improve the performance of autonomous driving semantic segmentation networks when combined with real data.
The paper introduces TextCrafter, a complex visual text generation framework that leverages novel "Text Insulation-and-Attention" mechanisms and a benchmark called CVTG-2K to achieve state-of-the-art performance in rendering accurate, multi-text images with fewer resources than industrial-scale models.
The paper introduces KARMMA, a lightweight multimodal knowledge distillation framework for egocentric action recognition that maintains high accuracy and robustness against missing modalities without requiring modality alignment during training or inference, making it ideal for on-robot deployment.
This paper investigates the optimal design parameters for procedural synthetic datasets to improve zero-shot stereo matching, resulting in an open-source dataset and generation system that outperforms training on mixed standard datasets and rivals the FoundationStereo dataset.