5475 papers
HeroGS is a unified framework that enhances robust 3D Gaussian Splatting under sparse-view conditions by employing a hierarchical guidance strategy across image, feature, and parameter levels to regularize Gaussian distributions, refine high-frequency details, and ensure geometric consistency, thereby achieving superior reconstruction fidelity compared to state-of-the-art methods.
This paper introduces ET-Turb, a large-scale synthetic dataset and a novel exposure-time-dependent modulation transfer function (ET-MTF) framework that models atmospheric turbulence blur as a continuous function of exposure time, thereby enabling more realistic turbulence synthesis and significantly improving the generalization of vision models on real-world data compared to existing methods.
UETrack is a unified and efficient single object tracking framework that leverages a Token-Pooling-based Mixture-of-Experts mechanism and Target-aware Adaptive Distillation to achieve superior speed-accuracy trade-offs across multiple modalities and hardware platforms.
FACE introduces a novel face-level autoregressive framework that treats each triangle as a single token to drastically reduce sequence length and computational costs while achieving state-of-the-art reconstruction quality and enabling efficient high-fidelity 3D mesh generation.
This paper presents InterCoG, a novel text-vision interleaved chain-of-grounding reasoning framework that enhances fine-grained image editing in complex multi-entity scenes by explicitly deducing target locations through text-based spatial reasoning before performing visual grounding and outcome specification, supported by a new dataset and auxiliary training modules to ensure spatial precision.
This paper introduces BiCAM, a bidirectional class activation mapping method that captures both supportive and suppressive contributions in Vision Transformers to enhance explanation faithfulness and enable efficient adversarial detection without retraining.
This paper introduces PromptStereo, a zero-shot stereo matching method that enhances the iterative refinement stage by integrating monocular structure and stereo motion cues as prompts into a Prompt Recurrent Unit (PRU), thereby achieving state-of-the-art generalization performance while preserving inherent monocular depth priors.
The paper introduces Nano-EmoX, a compact 2.2B-parameter multimodal language model trained via the Perception-to-Empathy (P2E) curriculum framework, which unifies six core affective tasks across a three-level cognitive hierarchy to achieve state-of-the-art performance in emotional intelligence from low-level perception to high-level empathy.
SimRecon is a novel framework that achieves high-fidelity, physically plausible compositional scene reconstruction from real videos by integrating a "Perception-Generation-Simulation" pipeline with two specialized bridging modules: Active Viewpoint Optimization for visual fidelity and a Scene Graph Synthesizer for physical plausibility.
This paper introduces OnlineX, a feed-forward framework that achieves unified online 3D reconstruction and semantic understanding by employing a decoupled active-to-stable state evolution paradigm to resolve cumulative drift while jointly modeling visual and language fields for real-time, high-fidelity performance.
The paper proposes HiFi-Inpaint, a novel framework that utilizes Shared Enhancement Attention and a Detail-Aware Loss to overcome data and supervision limitations, achieving state-of-the-art, high-fidelity generation of detail-preserving human-product images.
This paper introduces TimeGS, a novel time series forecasting framework that reframes prediction as 2D generative rendering by leveraging adaptive Gaussian kernels and a chronologically continuous rasterization mechanism to overcome the topological mismatches and resolution inefficiencies of existing 2D reshaping methods, thereby achieving state-of-the-art performance.
CamDirector is a novel video trajectory editing framework that achieves long-term temporal coherence and precise camera control by combining a hybrid warping scheme with a world cache and a history-guided autoregressive diffusion model, validated by a new benchmark called iPhone-PTZ.
This paper demonstrates that independent agents trained with predictive learning objectives on distinct viewpoints of the same environment naturally develop geometrically isomorphic latent spaces, enabling zero-shot knowledge transfer and efficient interoperability without parameter sharing or coordination.
This study presents a multimodal animal identification framework that leverages a massive dataset of 1.9 million images and synthetic textual descriptions to achieve an 84.28% Top-1 accuracy, representing an 11% improvement over unimodal baselines through systematic ablation of encoders and an optimal gated fusion strategy.
This paper proposes PDP, a novel prompt-decoupled framework for Incremental Object Detection that utilizes a dual-pool prompting paradigm to separate task-general and task-specific knowledge while employing a prototypical pseudo-label generation module to mitigate prompt drift, thereby achieving state-of-the-art performance on MS-COCO and PASCAL VOC benchmarks.
The paper introduces AutoFFS, a novel data-driven framework that utilizes adversarial free-form deformations to generate quantitative, counterfactual skull morphologies for objective and reproducible preoperative planning in Facial Feminization Surgery.
This paper presents a systematic evaluation of loss functions, architectures, and post-training strategies for long-tailed multi-label chest X-ray classification on the CXR-LT 2026 benchmark, demonstrating that LDAM-DRW combined with a ConvNeXt-Large backbone and classifier re-training achieves a top-5 ranking with 0.3950 mAP while offering practical insights into the development-to-test performance gap.
HAMMER is a novel framework that leverages multimodal large language models to achieve intention-driven 3D affordance grounding by aggregating interaction intentions into contact-aware embeddings and employing hierarchical cross-modal integration with multi-granular geometry lifting for accurate 3D localization.
This paper identifies that the ill-conditioned covariance of intermediate distributions in flow matching and score-based diffusion causes optimization bias and stagnation, and proposes reversible preconditioning maps to reshape this geometry, thereby enabling continued progress along suppressed directions and yielding better-trained models.