Multimodal Machine Learning Applications

Últimos artículos

• Visual Grounding in Zero-Shot Vision-Language Control

  J. de Curt\`o, Dayani Plasencia, Diego S\'anchez, I. de Zarz\`a · 7 de agosto de 2026

  Vision-language models (VLMs) are increasingly used as zero-shot controllers, but successful trajectories do not necessarily show that decisions are grounded in visual input: simulator dynamics and conservative action priors can produce favourable scores without meaningful perception. We investigate…

• iARCS: Iterative Agentic RL for Controllable 3D Scene Generation

  Saugat Adhikari, Ashok Prasad Neupane, Pramish Paudel, Ajad Chhatkuli, Danda Pani Paudel · 7 de agosto de 2026

  Synthetic 3D scene generation is increasingly used as a data source for computer vision and embodied AI, but existing generators often optimize perceptual realism without reliably satisfying task-critical functional constraints. This mismatch limits the usefulness of synthetic data for downstream tr…

• Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering

  Marco Morini, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara · 7 de agosto de 2026

  Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidence. However, retrieval often introduces noisy and partially relevant content, while images contain distracting visual reg…

• CoCo: Code as CoT for Text-to-Image Preview and Rare Concept Generation

  Haodong Li, Chunmei Qing, Huanyu Zhang, Dongzhi Jiang, Yihang Zou, Hongbo Peng, Dingming Li, Yuhong Dai, ZePeng Lin, Juanxi Tian, Yi Zhou, Siqi Dai, Jingwei Wu, Pheng-Ann Heng · 7 de agosto de 2026

  Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning. However, existing CoT-based T2I methods largely rely on abstract natural-language planning, which lacks the pr…

• PRISM: Priority-aware Rubric Internalization via Structured Multimodal Data Synthesis

  Xiaomin He, Dongling Xiao, Jiahao Xie, Ruiqi Lu, Qianle Wang, Zhongbin Guo, Wanxuan Sun · 7 de agosto de 2026

  Real-world multimodal instructions often bundle multiple requirements with unequal importance, yet most multimodal training data still reduce instruction following to answering one self-contained question. We study this gap through \textbf{rubric comprehension}, which casts the model not as a genera…

• The Illusion of Visual Tool-Use: A Causal Audit of Thinking with Images

  Zhiheng Wang, Bo Peng, Lai Wei, Chaochao Lu · 7 de agosto de 2026

  The "thinking-with-images" paradigm equips multimodal LLMs with active visual operations such as crop-and-zoom. However, models using these operations often achieve only marginal or negative gains over direct inference at substantially higher token cost. They may also repeatedly crop irrelevant regi…

• DistMedVL: Distributional Vision-Language Alignment for Uncertainty-Aware Medical Image Segmentation

  Jiaxuan Li, Qing Xu, Xiangjian He, Yue Li, Daokun Zhang, Fiseha B. Tesema, Rong Qu · 7 de agosto de 2026

  Cross-modal alignment of visual and textual representations is fundamental to multimodal medical image understanding, yet remains hindered by uncertainty in both modalities under real-world clinical conditions. Existing vision-language segmentation methods rely on deterministic cross-modal matching,…

• OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

  Qiushi Sun, Kanzhi Cheng, Yian Wang, Bowen Yang, Hang Yan, Liheng Chen, Fangzhi Xu, Zichen Ding, Nuo Chen, Jialin Cao, Xingdong Gong, Zehao Li, Kaiming Jin, Xinfeng Yuan, Zhoumianze Liu, Jingyang Gong, Zhangyue Yin, Jiahui Gao, Zhiyong Wu, Tianbao Xie, Jianbing Zhang, Ben Kao, Lingpeng Kong · 7 de agosto de 2026

  Computer-using agents (CUAs) are advancing rapidly across the digital world. A CUA trajectory records the agent's actions, states, and reasoning. Verifying whether it fulfilled the task instruction is central to CUA evaluation, data curation, and reinforcement learning. Neither human-written verifie…

• C$^3$PO: Evaluating Cross-Modal Composition and Counterfactual Performance in Omnimodal Models

  Swapnanil Mukherjee, Agyeya Negi, Tanuja Ganu, Ponnurangam Kumaraguru · 7 de agosto de 2026

  Current Multimodal Large Language Models (MLLMs) can process diverse sensory inputs, yet their reasoning remains heavily biased toward a dominant modality, resulting in brittle cross-modal reasoning. We introduce C$^3$PO, a benchmark of 3,404 samples spanning video, audio, image, and text, evaluatin…

• Seeing Is Not Deciding: Can Multimodal LLMs Act as Effective CEOs?

  Yuyang Dai, Xueqing Peng, Yuxia Wang, Preslav Nakov, Zhuohan Xie · 7 de agosto de 2026

  Large language models are increasingly applied as autonomous decision-making agents. However, in executive business decisions, existing benchmarks are limited to textonly settings. This makes it unclear whether models can perceive visual business evidence and effectively integrate it to improve deci…

• HoloCount: A Holistic Visual Counting Benchmark for MLLMs

  Jinhong Deng, Limeng Qiao, Guanglu Wan · 7 de agosto de 2026

  Visual counting is a fundamental pillar of multimodal intelligence, requiring a seamless integration of fine-grained grounding and spatial reasoning. While Multimodal Large Language Models (MLLMs) have achieved remarkable success in qualitative scene understanding, their quantitative precision remai…

• ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

  Jiafan Li, Mengxue Yang, Jiaqi Zhu, Liang Chang, Ying Li, Hongan Wang · 7 de agosto de 2026

  Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images. Traditional representation learning appro…

• VLMs for Videogame Data Annotation

  Katrin Schmid, Iuri Frosio · 7 de agosto de 2026

  Vision Language Models (VLMs) and Artificial Intelligence (AI) agents have revolutionized how engineers approach complex problems in real-world applications. Their adoption in video games is on the other hand limited by the extreme variability of the synthetic scenarios and their poor compliance wit…

• Training a Conditioned Video Game Agent on a VLM Annotated Dataset

  Katrin Schmid, Iuri Frosio · 7 de agosto de 2026

  Reinforcement Learning (RL) is a powerful but far from easy-to-use technique for policy learning. In the specific case of video games, access to the game engine is required to get rewards for training (e.g. to collect rewards from the environment). Furthermore, the proper identification and weightin…

• Beyond Global Routing Aggregation: Phase-Aware Expert Merging for MoE Vision-Language Models

  Hongyu Zhang, Cheng Yan, Xiang Xia, Wuyang Zhang · 6 de agosto de 2026

  Mixture-of-experts vision-language models (MoE-VLMs) increase model capacity with sparse expert activation, yet deployment requires storing the full expert pool. Training-free expert merging reduces this burden, and many routing-based methods aggregate routing statistics across all tokens to determi…

• Deltoris: Enabling Real-time VLA Inference in Embodied AI via Bit-level Sparsity and Speculative Inference

  Zheng Liu, Zeyu Guo, Zihan Liu, Anbang Wu, Han Zhao, Fangxin Liu, Zhezhi He, Yinhe Han, Jingwen Leng, Minyi Guo, Yiming Gan, Yu Feng · 6 de agosto de 2026

  Vision-language-action (VLA) models have emerged as a key component in embodied AI. Among existing approaches, diffusion-based VLA models achieve superior motion quality and generalization. However, diffusion-based VLA models are compute-intensive and must run at high control frequency, e.g., 50-200…

• OPD-V: Visual On-Policy Self-Distillation with Modality Balance

  Aniri, Jinhe Bi, Peng Liao, Zengjie Jin, Volker Tresp, Fei Shen, Yunpu Ma, Tat-Seng Chua · 6 de agosto de 2026

  On-Policy Self-Distillation (OPSD) has become a standard post-training approach for improving visual reasoning in multimodal large language models (MLLMs). Existing methods draw privileged information from diverse input sources to guide self-distillation. Yet these designs overlook Modality Imbalanc…

• GUARD: Grounding Uncertainty and Ablation-Based Risk Detection for Diffusion-Based VLAs

  Suhas Hegde, Jitendra Yasaswi Bharadwaj Katta · 6 de agosto de 2026

  Diffusion-based vision-language-action (VLA) policies can generate plausible actions even when their predictions are weakly grounded in the visual and language evidence defining the task. We introduce GUARD, a test-time failure detection method that measures this grounding without modifying the pret…

• GeoReward: Mitigating Contextual Variable Overestimation in Vision-Language Models for Cross-Market Preference Prediction

  Shuo Liu, Huixiang Cai, Weiru Zhang, Xiaoyi Zeng · 6 de agosto de 2026

  Vision-language models excel in many multimodal tasks but remain prone to a subtle yet impactful failure mode: they tend to overestimate dominant visual-textual cues while underestimating sparse but decision-critical contextual variables. This issue, which we term Contextual Variable Overestimation …

• Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

  Houze Xu, Jizhong Li, Ziyi Ye · 6 de agosto de 2026

  Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control. However, existing VLA models still face major challenges in long-horizon tasks: sparse expert demonstrations constrain cross-task compositional generalization…

• Teaching MLLMs to Say No: Generalized Referring Expression Comprehension via Refusal Calibrated GRPO

  Xuzheng Yang, Jun Ling, Tao Huang, Caiyan Qin, Peng Wang · 6 de agosto de 2026

  We tackle the challenging yet underexplored task of Generalized Referring Expression Comprehension (GREC), which requires a model to localize the object described by a textual expression when it exists (positive sample) and to refuse output when it does not (negative sample). Although Multimodal Lar…

• Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes

  Junlin Han, Shengbang Tong, David Fan, Minghao Chen, Philip Torr, Filippos Kokkinos, Mike Lewis · 6 de agosto de 2026

  Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining. Despite this momentum, the design space and the fundamental mechanisms of how modalities interact during unified training remain underexplored. We provide empirical clarity…

• DIVE: Dynamic Iterative Visual Evidence Construction for Efficient Vision-Language Models

  Chen Zhong, Xiao An, Zijie Wang, Jiepan Li, Guangyi Yang, Wei He · 6 de agosto de 2026

  Visual inputs in vision-language models (VLMs) are often encoded into substantially longer token sequences than text, making visual tokens a major bottleneck for efficient inference. Abundant recent methods address this bottleneck by scoring token importance and pruning low-scoring tokens in a singl…

• EndoVLM: An Endoscopy Vision-Language Pre-training Model via Anatomy-Guided Sparsity and Progressive Alignment

  Zhenyu Yi, Jianwei Xu, Yue Hu, Zhongwei Qiu, Sijing Li, Liang Huang, Bin Lv, Ling Zhang, Yingda Xia · 6 de agosto de 2026

  The development of foundation models (FMs) is crucial for advancing endoscopic image analysis. However, existing endoscopy FMs mainly rely on self-supervised learning from uni-modal images or videos, overlooking the rich semantic knowledge contained in clinical reports. Furthermore, effectively leve…

• Perception Before Reasoning: Dynamic Latent Reasoning for Video Understanding and Question Answering

  Haotian Xia, Zilin Xiao, Junbo Zou, Vicente Ordonez, Hanjie Chen · 6 de agosto de 2026

  Video question answering requires models to ground language queries in visual evidence and, when necessary, reason over that evidence across time. Existing methods typically rely on long textual chain-of-thought rationales, even though many questions can be answered as soon as the relevant object, a…