8111 papers
This paper introduces SGIFormer, a novel 3D instance segmentation method that combines Semantic-guided Mix Query initialization with a Geometric-enhanced Interleaving Transformer decoder to overcome existing limitations in query initialization and scalability, achieving state-of-the-art performance on major benchmarks while balancing accuracy and efficiency.
This paper proposes a parameter-efficient Open-Set Deepfake detection method that leverages a forgery-style mixture formulation and lightweight modules within a pre-trained Vision Transformer to achieve superior generalization across unseen forgery domains while significantly reducing computational costs.
This paper introduces the Abstracted Gaussian Prototypes (AGP) framework, a low-complexity, standalone system that achieves "true" one-shot learning by encoding visual concepts as Gaussian mixture models to simultaneously perform robust classification and generate human-indistinguishable novel class variants without relying on pre-training.
SplatSDF is a novel architecture that accelerates the convergence and improves the geometric accuracy of SDF-NeRF by directly fusing pre-trained 3D Gaussian splats into the network via a sparse injection strategy, enabling practical deployment on robotic systems without relying on consistency losses.
This paper introduces DGGS, a novel framework that achieves distractor-free generalizable 3D Gaussian Splatting by employing a scene-agnostic mask prediction module during training and a two-stage reference scoring with pruning mechanism during inference to ensure stable, high-quality reconstruction in unseen scenes.
This paper proposes a framework that enhances Open Vocabulary Object Detection models for open-world settings by introducing Pseudo Unknown Embedding and Multi-Scale Contrastive Anchor Learning to identify and incrementally learn novel objects, thereby addressing limitations in detecting far-out-of-distribution items and reducing misclassifications while maintaining state-of-the-art performance.
This paper proposes a novel text-to-sketch-animation method that leverages a pre-trained text-to-video diffusion model guided by SDS loss, while introducing length-area regularization for temporal consistency and As-Rigid-As-Possible loss to preserve sketch topology, thereby outperforming state-of-the-art approaches in both quantitative and qualitative evaluations.
The paper introduces PPT, a scalable pretraining framework that leverages automatically generated pseudo-labeled trajectories from off-the-shelf detectors to enhance motion forecasting models' performance and generalization, particularly in low-data and cross-domain scenarios, while reducing reliance on costly manual annotations.
The paper proposes IV-tuning, a parameter-efficient transfer learning framework that leverages pre-trained visual models with only 3% trainable backbone parameters to overcome the generalization limitations of full fine-tuning and achieve state-of-the-art performance across various infrared-visible tasks.
This paper proposes MomentMix, a data augmentation strategy combining ForegroundMix and BackgroundMix, and a Length-Aware Decoder to address feature diversity limitations and prediction biases, thereby significantly improving the localization accuracy of short moments in Video Moment Retrieval tasks.
This paper proposes JOSH, an optimization-based method that jointly reconstructs 4D human motion and surrounding scenes from monocular web videos by leveraging human-scene contact constraints, along with its efficient learning-based variant JOSH3R trained on pseudo-labels derived from JOSH.
This paper challenges the prevailing reliance on diffusion models for adversarial defense by demonstrating that non-diffusion purifiers can achieve superior robustness, transferability, and cross-dataset generalization, notably outperforming ImageNet-trained diffusion models when applied to ImageNet despite being trained only on CIFAR-10.
This paper introduces Dual-IPO, an iterative framework that simultaneously and progressively optimizes both a CoT-guided reward model and a video generation model to enhance text-to-video synthesis quality and human preference alignment without requiring extensive manual annotations.
The paper proposes RelaCtrl, a relevance-guided framework that optimizes control signal integration in Diffusion Transformers by dynamically tailoring layer configurations and introducing a Two-Dimensional Shuffle Mixer, achieving superior performance with only 15% of the parameters and computational complexity compared to PixArt-delta.
This paper introduces U-F-CBM, a novel method that transforms any frozen visual classifier into an unsupervised, label-free, and CLIP-free Concept Bottleneck Model by aligning its class distribution with vision-language counterparts, thereby achieving state-of-the-art performance without requiring manual annotations or pre-trained CLIP models.
UniFuture introduces a unified 4D driving world model that jointly generates future RGB images and depth maps through a dual-latent sharing scheme and multi-scale latent interaction, achieving superior performance in both dynamic scene forecasting and geometric perception compared to existing specialized models.
Inspired by the convolution theorem, this paper analyzes gating mechanisms from a frequency perspective to reveal their role in managing frequency responses, leading to the proposal of GmNet, a lightweight model that mitigates low-frequency bias and achieves high performance in image classification.
The paper introduces ViT-Linearizer, a cross-architecture distillation framework that transfers the rich representations of quadratic-complexity Vision Transformers into efficient linear-time recurrent models (such as Mamba) via activation matching and masked prediction, achieving competitive ImageNet accuracy while significantly reducing inference costs for high-resolution tasks.
The paper introduces LAMM-ViT, a novel Vision Transformer that enhances AI face detection by integrating Region-Guided Multi-Head Attention with dynamic Layer-aware Mask Modulation to capture hierarchical structural inconsistencies across diverse generative models, achieving state-of-the-art generalization performance.
This paper proposes a Reflectance Prediction-based Knowledge Distillation (RPKD) framework that enhances 3D object detection robustness in low-bitrate compressed point clouds by discarding reflectance during transmission, reconstructing it via a geometry-based prediction module, and utilizing a cross-source distillation strategy to transfer knowledge from raw to compressed data.