1872 papers
This study evaluates the effectiveness of vision-language and large language models in converting scanned student-drawn automata diagrams into TikZ code, finding that while direct image-to-text generation often yields errors, human-corrected descriptions significantly improve the accuracy of the resulting digital diagrams for educational applications like automated grading.
The paper introduces , a novel map-free visual localization framework that achieves high accuracy and robustness in sparse, wild scenes by leveraging online feed-forward 3D reconstruction and two-stage pose refinement, thereby eliminating the need for offline scene preprocessing and storage.
VisualAD is a language-free, zero-shot anomaly detection framework that leverages a frozen Vision Transformer backbone with learnable normality and abnormality tokens, along with spatial-aware cross-attention and self-alignment modules, to achieve state-of-the-art performance across industrial and medical domains without relying on text encoders or cross-modal alignment.
The paper introduces SGG-R, a structured reasoning framework that combines chain-of-thought-guided supervised fine-tuning with relation augmentation and a novel dual-granularity reward scheme in reinforcement learning to achieve end-to-end unbiased Scene Graph Generation with improved recall and reduced bias on long-tailed distributions.
This paper introduces EcoG-Bench, a rigorous bilingual benchmark for egocentric co-speech grounding that reveals a significant performance gap between humans and state-of-the-art MLLMs, highlighting how multimodal interface limitations rather than reasoning deficits hinder the alignment of speech with pointing gestures in situated collaboration.
This paper presents a device-agnostic surgical tool tracking framework that fuses multiple sensing modalities within a dynamic scene graph to overcome line-of-sight occlusions and enhance the robustness of augmented reality visualization in operating rooms.
The paper introduces AutoReg3D, an autoregressive 3D object detector that reformulates LiDAR-based detection as a sequence generation task using a near-to-far ordering to eliminate reliance on hand-crafted components like anchors and NMS, thereby achieving competitive performance while enabling the integration of advanced language model techniques such as reinforcement learning.
TeamHOI is a decentralized framework that leverages a Transformer-based policy and a masked Adversarial Motion Prior strategy to enable a single unified policy to control scalable, physically realistic cooperative human-object interactions among any number of humanoid agents.
AutoTraces is a novel autoregressive vision-language-trajectory model that leverages multimodal large language models with a specialized trajectory tokenization scheme and automated chain-of-thought reasoning to achieve state-of-the-art, long-horizon robot trajectory forecasting in human-populated environments.
This paper proposes the ViSA-enhanced framework, a triple-phase collaborative architecture that leverages structured visual prompting to enable Vision-Language Models to perform direct spatial reasoning on image planes, achieving a 70.3% improvement in success rate over state-of-the-art aerial Vision-Language Navigation methods on the CityNav benchmark.
This paper addresses the significant challenge of analog clock reading in state-of-the-art Vision-Language Models by introducing the real-world, diverse TickTockVQA dataset and the Swap-DPO fine-tuning framework, which together substantially improve spatial-temporal reasoning and accuracy under complex visual conditions.
This paper proposes "Missing No More," a novel dictionary-guided framework that addresses the challenge of missing infrared modality in image fusion by learning a shared convolutional dictionary to enable interpretable coefficient-domain inference and fusion, thereby avoiding uncontrolled pixel-space generation while improving perceptual quality and downstream detection performance.
VSDiffusion is a visibility-constrained two-stage diffusion framework that addresses the ill-posed nature of shadow generation by incorporating visibility priors through a shadow-gated cross-attention branch and a learned soft prior map to produce geometrically consistent and realistic cast shadows.
AffordGrasp is a diffusion-based framework that generates physically stable and semantically accurate human grasps by integrating affordance-aware latent representations with a dual-conditioning process to bridge the modality gap between 3D object geometry and textual interaction instructions.
This paper introduces MambaDance, a novel dance generation framework that replaces Transformers with a Mamba-based diffusion model and employs a Gaussian-based beat representation to effectively capture the sequential, rhythmic, and music-synchronized nature of dance across varying sequence lengths.
This paper proposes a two-stage cascaded framework that generates controllable complex human motion videos by first using an autoregressive model to synthesize 2D skeleton sequences from text descriptions and then employing a pose-conditioned diffusion model with adaptive layer fusion to render high-fidelity videos, supported by a new synthetic dataset designed to overcome limitations in existing benchmarks.
QualiTeacher introduces a novel framework for real-world image restoration that transforms imperfect pseudo-labels into conditional supervisory signals by explicitly conditioning the student model on estimated label quality, thereby enabling the learning of a quality-graded restoration manifold that avoids artifact mimicry and achieves superior generalization.
This paper presents a robust multimodal framework for the 10th ABAW Expression Recognition Challenge that utilizes a dual-branch Transformer with safe cross-attention and modality dropout to dynamically fuse audio and visual data, effectively addressing partial occlusions, missing modalities, and class imbalance to achieve 60.79% accuracy on the Aff-Wild2 validation set.
Speed3R is a sparse feed-forward 3D reconstruction model that overcomes the quadratic computational bottleneck of dense attention by employing a dual-branch mechanism to focus on informative tokens, achieving a 12.4x inference speedup with minimal accuracy trade-offs.
This paper introduces "See & Switch," a vision-based interactive framework for Programming by Demonstration that utilizes eye-in-hand images to enable reliable online conditional branching and anomaly detection in dexterous robot tasks, achieving high accuracy across diverse conditions and novice users.