Ophthalmology

Semantic Generative Tuning for Unified Multimodal Models

Unified multimodal models (UMMs) strive to consolidate visual understanding and visual generation within a single architecture. However, prevailing training paradigms independently optimize understanding via sparse text signals and generation through dense pixel objectives. Such a decoupled strategy yields misaligned representation spaces, isolating visual understanding from generation and hinderi...

Vision-OPD: Learning to See Fine Details for Multimodal LLMs via On-Policy Self-Distillation

Multimodal Large Language Models (MLLMs) still struggle with fine-grained visual understanding, where answers often depend on small but decisive evidence in the full image. We observe a regional-to-global perception gap: the same MLLM answers fine-grained questions more accurately when conditioned on evidence-centered crops than on the corresponding full images, suggesting that many failures stem ...

Prior scene context reshapes feature reliance during rapid perception

Human perception is shaped by both sensory input and prior knowledge or expectations. But how does prior contextual information influence rapid visual...

Systematic Evaluation of Vision Transformers for Automated Cervical Cancer Classification: Optimization, Statistical Validation, and Clinical Interpretability

Manual Pap smear analysis for cervical cancer screening is limited by inter-observer variability, time constraints, and restricted expert availability...

Medical Context Distorts Decisions in Clinical Vision Language Models

Vision-language models (VLMs) are increasingly proposed for clinical decision support, yet their reliability in real-world scenarios that require inte...

FastOCR: Dynamic Visual Fixation via KV Cache Pruning for Efficient Document Parsing

Vision-Language Models (VLMs) have shown strong promise on Optical Character Recognition (OCR), yet the sheer number of visual tokens required to enco...

A Distributional View for Visual Mechanistic Interpretability: KL-Minimal Soft-Constraint Principle

Most current paradigms in visual mechanistic interpretability (MI) remain confined to interpreting internal units of the vision model via heuristic me...

Benchmarking long-context genome language models on biosynthetic gene clusters

Recent advances in language models for natural language processing have spread to the field of genomics, driving the development of genome language mo...

A Picture is Worth a Thousand Words? An Empirical Study of Aggregation Strategies for Visual Financial Document Retrieval

Visual RAG has offered an alternative to traditional RAG. It treats documents as images and uses vision encoders to obtain vision patch tokens. Howeve...

Do We Really Need External Tools to Mitigate Hallucinations? SIRA: Shared-Prefix Internal Reconstruction of Attribution

Large vision-language models (LVLMs) often hallucinate when language priors dominate weak or ambiguous visual evidence. Existing contrastive decoding ...

MiVE: Multiscale Vision-language features for reference-guided video Editing

Reference-guided video editing takes a source video, a text instruction, and a reference image as inputs, requiring the model to faithfully apply the ...

Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation

In recent years, computer vision has witnessed remarkable progress, fueled by the development of innovative architectures such as Convolutional Neural...

Exploring Vision-Language Models for Online Signature Verification: A Zero-Shot Capability Study

Recent advancements in Vision-Language Models (VLMs) have demonstrated strong capabilities in general visual reasoning, yet their applicability to rig...

BCI-Based Assessment of Ocular Response Time Using Dynamic Time Warping Leveraging an RDWT-Driven Deep Neural Framework

Mild traumatic brain injury (mTBI) is a prevalent condition that remains difficult to diagnose in its early stages. Oculomotor dysfunction is a well-e...

MemLens: Benchmarking Multimodal Long-Term Memory in Large Vision-Language Models

Memory is essential for large vision-language models (LVLMs) to handle long, multimodal interactions, with two method directions providing this capabi...

Representative Attention For Vision Transformers

Linear attention has emerged as a promising direction for scaling Vision Transformers beyond the quadratic cost of dense self-attention. A prevalent s...

Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model

Vision-Language-Action models have emerged as a promising paradigm for robotic manipulation by unifying perception, language grounding, and action gen...

TCP-SSM: Efficient Vision State Space Models with Token-Conditioned Poles

State Space Models (SSMs) have emerged as a compelling alternative to attention models for long-range vision tasks, offering input-dependent recurrenc...

Allegory of the Cave: Measurement-Grounded Vision-Language Learning

Vision-language models typically reason over post-ISP RGB images, although RGB rendering can clip, suppress, or quantize sensor evidence before infere...

Revisiting Shadow Detection from a Vision-Language Perspective

Shadow detection is commonly formulated as a vision-driven dense prediction problem, where models rely primarily on pixel-wise visual supervision to d...