6132 papers
The paper proposes TenExp, a novel unsupervised framework that leverages a mixture-of-experts approach to dynamically search for and activate optimal single or mixed tensor decompositions, thereby overcoming the limitations of existing methods confined to fixed factor-interaction families.
This paper proposes the Spatial and Frequency Domain Enhancement Network (SFDE), a lightweight three-branch architecture that leverages complementary spatial and frequency domain representations to effectively address geometric asymmetry and texture inconsistencies in cross-view geo-localization, achieving state-of-the-art performance through multiscale structural modeling and frequency invariance.
This paper introduces RSHBench, a benchmark for diagnosing hallucinations in remote sensing visual question-answering, and proposes RADAR, a training-free inference method that leverages intrinsic attention to improve grounding and reduce hallucinations in multimodal large language models.
This paper proposes HiLoRA, a hierarchical Low-Rank Adaptation framework for Federated Learning that leverages a three-tier adapter structure and subspace-based client clustering to effectively capture global, subgroup, and client-specific knowledge, thereby enhancing both personalization and generalization in Vision Transformer models.
The paper introduces UNICORN, a unified public benchmark featuring a standardized two-step evaluation framework and a novel aggregate metric to systematically assess the cross-modality and cross-task generalization of medical foundation models across diverse imaging and natural language data from multiple institutions.
R3GW introduces a novel method for reconstructing outdoor scenes from unconstrained photo collections by separating the scene into relightable foreground and non-reflective sky components, enabling state-of-the-art physically based relighting and high-quality novel view synthesis under arbitrary illumination conditions.
This paper presents NOVA, a pair-free video editing framework that combines sparse user-provided keyframe guidance with dense motion and texture synthesis, trained via a degradation-simulation strategy to achieve high edit fidelity and temporal consistency without requiring large-scale paired datasets.
This paper addresses the limitations of visual language models in recognizing the complex layouts of Ancient Greek critical editions by introducing a large-scale synthetic corpus and a real-world benchmark, demonstrating that while zero-shot performance lags behind traditional tools, fine-tuned models like Qwen3VL-8B can achieve state-of-the-art accuracy.
The paper introduces ScribeTokens, a fixed-vocabulary tokenization method for digital ink that decomposes pen movements into unit pixel steps, demonstrating superior performance over vector representations in both handwritten text generation and recognition, particularly when enhanced by a novel next-ink-token prediction pretraining strategy.
This paper introduces GaussDerResNets, a novel deep learning architecture that combines scale-covariant Gaussian derivative layers with residual skip connections to achieve provable scale invariance and superior generalization to unseen image scales across multiple datasets while maintaining high accuracy and computational efficiency.
By probing LVLMs with a synthetic directed graph dataset, this study reveals that while node and structural information are linearly encoded early in the vision encoder, edge representations emerge only later in the language model's text tokens, explaining the models' persistent struggles with relational understanding.
This paper introduces a multimodal-prior-guided importance sampling framework for hierarchical 3D Gaussian Splatting that fuses photometric, semantic, and geometric cues to strategically refine sparse-view novel view synthesis, thereby achieving state-of-the-art reconstruction quality while mitigating overfitting and noise.
SIGMark is a scalable, distortion-free in-generation watermarking framework for video diffusion models that enables blind extraction and robustness against temporal disturbances through Global Frame-wise PseudoRandom Coding and a Segment Group-Ordering module tailored for causal 3D VAEs.
The paper proposes SemanticDialect, a semantic-aware mixed-format quantization framework for Video Diffusion Transformers that utilizes block-wise format selection, activation decomposition with residual correction, and semantic token grouping to achieve high-quality video generation with significantly reduced memory and compute costs.
StegaFFD is a privacy-preserving face forgery detection framework that embeds facial images into natural cover images using steganography to avoid suspicion, while employing specialized decomposition, attention, and alignment mechanisms to accurately detect forgeries within the steganographic domain despite semantic interference.
This paper introduces LLandMark, a modular multi-agent framework that enhances multimodal video retrieval for complex, real-world queries by integrating specialized agents for landmark detection, LLM-driven image generation, and OCR refinement to achieve culturally grounded and explainable results, particularly for Vietnamese scenes.
MVD-HuGaS is a novel framework that achieves state-of-the-art free-view 3D human rendering from a single image by leveraging a fine-tuned multi-view diffusion model to generate consistent multi-view images, an alignment module for joint Gaussian and pose optimization, and a depth-based facial distortion mitigation module to ensure high-fidelity reconstruction.
This paper introduces 3D-DRES, a new task for fine-grained phrase-to-3D-instance mapping, supported by the DetailRefer dataset with pioneering phrase-instance annotations and the DetailBase baseline, which together enhance 3D vision-language understanding and improve performance on traditional 3D-RES benchmarks.
The paper proposes ProGIC, a progressive and lightweight generative image compression codec based on residual vector quantization and a compact backbone, which achieves significant bitrate savings, faster encoding/decoding speeds, and flexible progressive transmission compared to existing methods.
This paper introduces the Harmonic Beltrami Signature Network (HBSN), a novel deep learning architecture that efficiently computes rotation-, scale-, and translation-invariant shape representations to serve as a versatile shape prior module, thereby enhancing the performance of existing computer vision segmentation models.