6478 papers
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.
MERG3R is a training-free, model-agnostic divide-and-conquer framework that enables neural visual geometry models to scale to large, unordered image collections by partitioning data into manageable subsets and merging local reconstructions into a globally consistent 3D model, thereby overcoming GPU memory limitations while improving accuracy and scalability.
This paper investigates the performance degradation of existing Video Moment Retrieval methods when transitioning from caption-based to search queries, identifies language and multi-moment gaps alongside a decoder-query collapse as key causes, and proposes architectural modifications to significantly improve generalization on multi-moment search queries.
This paper proposes a transparent, patient-specific radiomic framework that employs a two-stage retrieval strategy to select compact, complementary feature sets for knee MRI diagnosis, achieving performance competitive with deep learning models while offering enhanced interpretability through auditable links between specific anatomical regions and clinical outcomes.
This paper introduces "Cultural Counterfactuals," a high-quality synthetic dataset of nearly 60,000 images created by placing diverse individuals into varied cultural contexts to enable the precise measurement and evaluation of cultural biases related to religion, nationality, and socioeconomic status in Large Vision-Language Models.