3918 papers
This paper identifies a "linguistic blindness" failure mode in Vision-Language-Action (VLA) models where they ignore contradictory instructions in favor of visual priors, and proposes IGAR, a train-free attention recalibration method that effectively restores language grounding and prevents erroneous actions without requiring model retraining.
The paper introduces RepKAN, a novel dual-path architecture that combines CNN efficiency with KAN's non-linear power to achieve state-of-the-art performance and explicit physical interpretability in remote sensing image classification.
EffectMaker is a unified reasoning-generation framework that leverages a multimodal large language model and a diffusion transformer, supported by a large-scale synthetic dataset called EffectData, to enable scalable, high-quality, and controllable customized visual effect creation without requiring per-effect fine-tuning.
The paper introduces MOSIV, a novel framework that leverages differentiable simulation and geometry-aligned objectives to identify continuous, per-object material parameters from videos of complex multi-object interactions, outperforming existing methods on a new synthetic benchmark.
ViewFusion is a two-stage framework that enhances multi-view spatial reasoning in vision-language models by explicitly separating cross-view spatial pre-alignment from question-driven reasoning, achieving significant accuracy improvements on benchmarks like MMSI-Bench through synthetic supervision and reinforcement learning.
StruVis is a novel, generator-agnostic framework that enhances reasoning-based text-to-image generation by enabling MLLMs to utilize text-based structured visual representations as intermediate reasoning states, thereby overcoming the limitations of existing text-only and text-image interleaved approaches to achieve significant performance gains on benchmarks.
This paper introduces Occlusion-Aware SORT (OA-SORT), a training-free, plug-and-play framework that enhances multi-object tracking robustness by analyzing occlusion status through a Gaussian Map to mitigate positional cost confusion and estimation instability, thereby significantly improving performance across multiple datasets.
This paper addresses the computational challenges of using dense hypercolumns for image segmentation by introducing stratified subsampling and ensemble learning, demonstrating that these methods significantly outperform standard UNet baselines, particularly in low-shot scenarios where a simple Logistic Regression classifier achieves the best results.
This paper introduces FontUse, a data-centric approach that leverages a large-scale, automatically annotated dataset of 70K images with style- and use-case-conditioned prompts to fine-tune text-to-image models, significantly improving their ability to generate typography that accurately reflects requested visual attributes without requiring architectural changes.
This paper proposes GvU, a self-supervised reinforcement learning framework that leverages a unified multimodal model's own understanding branch as an intrinsic reward signal to iteratively improve its text-to-image generation quality, thereby narrowing the capability gap between visual understanding and generation.
GenHOI is a lightweight augmentation for pretrained video generation models that enhances object-consistent hand-object interaction in in-the-wild scenarios by employing Head-Sliding RoPE for temporally balanced reference injection and a two-level spatial attention gate for selective focus on interaction regions.
The paper introduces Curious-VLA, a two-stage framework that overcomes the exploration limitations of standard driving VLA models by employing Feasible Trajectory Expansion during imitation learning and Adaptive Diversity-Aware Sampling with a Spanning Driving Reward during reinforcement learning, achieving state-of-the-art performance on the Navsim benchmark.
This paper investigates failure modes in lightweight Vision-Language Models for automated driving by analyzing intermediate activations to reveal that while some visual concepts like object presence are linearly encoded, others like orientation are not, leading to either perceptual or cognitive failures that are further exacerbated by object distance.
TempoSyncDiff is a reference-conditioned latent diffusion framework that employs teacher-student distillation and temporal regularization to enable low-latency, temporally stable, and identity-consistent audio-driven talking head generation suitable for edge deployment.
This paper presents a novel pipeline that transforms archived omnidirectional RGB-LiDAR logs into robust 3D Gaussian Splatting initialization assets by addressing sensor distortion and data density challenges through ERP-to-cubemap conversion, color-stratified downsampling, and multi-modal registration, thereby enabling the creation of high-fidelity digital twins from standard, underutilized sensor data.
This paper introduces the novel task of Emotionally Continuous Talking Face Generation (EC-TFG) and proposes the TIE-TFG model, which utilizes text and varying emotion descriptions to synthesize realistic videos featuring smooth, natural emotional transitions rather than fixed expressions.
This paper proposes "Lyapunov Probes," a novel hallucination detection method for Large Language Models that frames the problem using dynamical systems stability theory to identify unstable knowledge-transition regions where hallucinations occur.
DeepSight is the first dedicated depth-driven multimodal large language model that leverages single-channel depth maps, a novel depth instruction dataset, and a modified ViT encoder to significantly enhance 3D scene understanding and spatial reasoning capabilities.
This paper proposes a neural video compression method for static scenes that incorporates positive-incentive noise to disentangle transient variations from persistent backgrounds, achieving a 73% bitrate reduction while maintaining pixel-level fidelity and avoiding hallucinated details.
FedARKS is a novel federated learning framework for person re-identification that overcomes the limitations of global feature reliance and uniform averaging by introducing Robust Knowledge and Knowledge Selection mechanisms to capture subtle domain-invariant details and prioritize high-quality client contributions for improved domain generalization.