2562 papers
The paper introduces GameVerse, a comprehensive benchmark featuring a novel reflect-and-retry paradigm and a hierarchical taxonomy across 15 games, demonstrating that Vision-Language Models can effectively improve their gameplay policies through video-based reflection by combining failure trajectories with expert tutorials.
The paper introduces ASMIL, a unified framework that addresses unstable attention dynamics, overfitting, and over-concentrated attention in attention-based multiple instance learning for whole slide imaging by employing an anchor model with a normalized sigmoid function and token random dropping, resulting in significant performance improvements over state-of-the-art methods.
The paper introduces EnsAug, a novel training paradigm that improves human motion analysis by replacing the conventional single-model approach with an ensemble of specialists, each trained on data augmented by a distinct geometric transformation, thereby achieving state-of-the-art performance while respecting kinematic constraints.
The paper introduces HyperTokens, a transformer-based token generator augmented with meta-inspired regularizers and causal auxiliary supervision to enable efficient, low-forgetting continual Video-Language Understanding by dynamically producing task-specific prompts without increasing memory costs.
The paper proposes Graph-of-Mark (GoM), a novel pixel-level visual prompting technique that overlays scene graphs onto images to capture object relationships, thereby significantly enhancing the spatial reasoning and zero-shot performance of multimodal language models.
This paper introduces a system-level inference optimization for Diffusion Transformer-based video generation that employs a sequence-parallel Causal-RoPE mechanism and operator fusion to overcome memory and latency bottlenecks, achieving near real-time speeds and sub-second first-frame latency on an eight-GPU cluster.
This paper reveals that Chain-of-Thought prompting often underperforms direct answering in medical visual question answering due to a "medical perception bottleneck," and proposes training-free grounding interventions to restore visual accuracy and improve model reasoning.
This paper introduces SJD-PV, a training-free acceleration framework for autoregressive image generation that leverages phrase-level speculative verification based on token co-occurrence statistics to jointly validate multiple correlated tokens, achieving up to 30% faster decoding without compromising visual fidelity.
The paper proposes CalibFusion, a Transformer-based differentiable calibration framework that learns to implicitly refine Radar-Camera extrinsics end-to-end to overcome the challenges of textureless, cluttered water-surface environments and significantly improve fusion-based 2D object detection.
This paper investigates whether semantic noise initialization, known to improve image diffusion models, transfers to text-to-video generation, finding that while it shows a slight positive trend on temporal metrics, it does not significantly outperform standard Gaussian noise due to weak or unstable signals in the noise space.
This paper proposes an unsupervised CNN autoencoder with a novel weighted spectral angle distance loss to enable blind, automated unmixing of complex ATR-FTIR hyperspectral images from historical oil painting cross-sections, significantly improving the interpretability and scalability of material analysis compared to traditional manual methods.
AutoFigure-Edit is an end-to-end system that generates fully editable, high-quality scientific illustrations from long-form text with flexible style adaptation via reference images, leveraging long-context understanding and native SVG support to overcome limitations in editability and efficiency found in existing automated tools.
This paper proposes a hybrid few-shot learning model integrating Siamese and Prototypical Networks with Grad-CAM-based Explainable AI to achieve high-accuracy, interpretable classification of maize, rice, and wheat leaf diseases under limited data conditions.
This paper addresses the limitations of current Large Vision-Language Models in deep chart research by proposing Parallel Relative Policy Optimization (PRPO) to resolve training conflicts and constructing the MCDR-Bench evaluation framework to enable objective assessment of complex reasoning capabilities.
The paper introduces MultiGen, a novel diffusion-based game engine that incorporates an explicit, persistent external memory to enable user-editable world structures and support coherent, real-time multiplayer interactions, overcoming the limitations of conventional next-frame prediction models.
This paper introduces VB, a novel benchmark designed to evaluate vision-language models' ability to determine image visibility and appropriately abstain from answering when evidence is insufficient, utilizing controlled minimal edits and specialized metrics to reveal that top-tier models like GPT-4o and Gemini 3.1 Pro significantly outperform open-source alternatives in confidence-aware accuracy and perspective reasoning.
The paper introduces RADAR, a multimodal benchmark comprising expert-annotated 3D abdominal CT scans and radiology report edits that enables the systematic evaluation of AI models on fine-grained clinical reasoning tasks, specifically image-text alignment and discrepancy assessment during the radiology report review process.
The paper proposes ECHO, a multi-agent framework that utilizes iterative hypergraph operations and a "Link-then-Bind" strategy to mitigate cascading errors in Multimedia Event Extraction, achieving significant performance improvements over state-of-the-art methods on the M2E2 benchmark.
This paper presents an innovative, mobile-device-based 3D imaging approach using Structure-from-Motion and segmentation algorithms to reconstruct and analyze aggregate size and shape from stockpiles, offering a convenient and affordable solution for onsite quality assurance in road construction and geotechnics.
This paper introduces TimeSpot, a comprehensive benchmark comprising 1,455 real-world images from 80 countries designed to evaluate the limited geo-temporal reasoning capabilities of current vision-language models in predicting location, time, and environmental context from visual evidence alone.