3901 papers
FTSplat is a feed-forward framework that directly predicts continuous, simulation-ready triangle surfaces from multi-view images in a single pass, overcoming the optimization bottlenecks of NeRF and 3DGS while providing the explicit manifold geometry required for robotics and simulation.
The paper proposes OD-RASE, an ontology-driven framework that leverages large-scale visual language models and diffusion models to proactively identify accident-prone road structures and generate reliable infrastructure improvement proposals, thereby enhancing the safety of autonomous driving systems.
This paper proposes a novel Residual Masking Network that integrates a Deep Residual Network with a Unet-like segmentation architecture to refine feature maps via an attention mechanism, achieving state-of-the-art accuracy on the FER2013 and VEMO facial expression recognition datasets.
The paper proposes SLER-IR, a novel all-in-one image restoration framework that utilizes spherical layer-wise expert routing, a spherical uniform degradation embedding with contrastive learning, and a global-local granularity fusion module to effectively overcome feature interference and spatial non-uniform degradations, achieving state-of-the-art performance across multiple restoration tasks.
This paper proposes a robust skew estimation method for document images using Adaptive Radial Projection on the 2D Discrete Fourier Magnitude spectrum, introduces the DISE-2021 dataset for evaluation, and demonstrates that the approach outperforms existing methods.
LucidNFT is a multi-reward reinforcement learning framework for generative real-world super-resolution that addresses faithfulness hallucinations and optimization bottlenecks by introducing a degradation-robust consistency evaluator, a decoupled advantage normalization strategy, and a large-scale real-degradation dataset to achieve superior perceptual-faithfulness trade-offs.
The paper proposes E-AdaPrune, an energy-driven adaptive token pruning framework that dynamically allocates visual token budgets based on spectral energy to improve Vision-Language Model efficiency and performance without adding learnable parameters or significant latency.
This paper introduces VINE, a unified framework for few-shot segmentation that leverages spatial-view graphs and discriminative priors to refine class-specific prototypes, effectively addressing structural misalignment and cross-view inconsistency to generate accurate masks even under challenging viewpoint variations.
The paper introduces OVGGT, a training-free framework that enables constant-memory and constant-compute streaming 3D geometry reconstruction from arbitrarily long videos by combining Self-Selective Caching and Dynamic Anchor Protection to overcome the quadratic cost and unbounded memory growth of existing transformer-based methods.
This paper proposes a streamlined, training-free two-stage open-vocabulary object recognition framework that combines object segmentation with CLIP-based and CNN/MLP-based feature alignment, demonstrating that a CLIP-only approach without Singular Value Decomposition achieves state-of-the-art performance on standard benchmarks.
This paper introduces Skeleton-to-Image Encoding (S2I), a novel method that transforms heterogeneous 3D skeleton sequences into standardized image-like formats to leverage powerful vision-pretrained models for effective self-supervised skeleton representation learning and cross-modal action recognition.
This paper proposes CR-QAT, a framework combining curriculum-based progressive quantization and text-centric relational knowledge distillation to mitigate the severe performance degradation of naive low-bit quantization in open-vocabulary object detection, thereby achieving significant accuracy improvements on zero-shot benchmarks.
PROBE is a learning-free LiDAR place recognition descriptor that models BEV occupancy as Bernoulli random variables and analytically marginalizes over continuous translations via the polar Jacobian to achieve sensor-independent, rotation-robust 3D place recognition with state-of-the-art performance among handcrafted methods.
The paper introduces ProCap, a novel framework that improves change captioning by reformulating static image comparison into dynamic procedure modeling through a two-stage design that learns latent change dynamics from sparse keyframes and utilizes learnable procedure queries to generate temporally coherent descriptions of how changes occur.
This paper introduces DynUAV, a comprehensive benchmark featuring over 1.7 million annotations across 42 video sequences to address the limitations of existing datasets in evaluating multi-object tracking under the intense ego-motion, scale variations, and motion blur characteristic of complex UAV operations.
This paper presents a dual-branch framework with Gram Regularization Loss and an anime-specific Tagger Network to directly minimize the distribution shift between training and inference data, achieving state-of-the-art high-resolution, disentangled, and controllable reference-based sketch colorization.
This paper introduces HarvestFlex, the first study demonstrating that vision-language-action policies can be successfully adapted to real-world greenhouse strawberry harvesting using a closed-loop system with three-view RGB sensing and minimal teleoperated data, achieving a 74.0% success rate without relying on depth sensors or explicit geometric calibration.
This technical report details a collaboration with industry leaders in Pakistan's Sialkot surgical cluster to develop an Automated Optical Inspection system using deep learning models (YOLOv8, ResNet-152, and EfficientNet-b4) on a new dataset of 4,414 images to detect manufacturing defects in surgical instruments, thereby enhancing patient safety and manufacturing quality.
This paper presents MM-ISTS, a multimodal framework that leverages vision-text large language models and a novel two-stage encoding mechanism to enhance irregularly sampled time series forecasting by integrating temporal, visual, and textual modalities for improved pattern recognition and contextual understanding.
RePer-360 is a distortion-aware self-modulation framework that adapts perspective-trained depth foundation models to 360° panoramic depth estimation by preserving pretrained priors through a lightweight geometry-aligned guidance module and a Self-Conditioned AdaLN-Zero mechanism, achieving superior performance with only 1% of the training data.