Search: “multimodal”

arXiv:2606.00168v1 Announce Type: new Abstract: AI systems are increasingly deployed in conversational settings where users may be uncertain whether they are speaking with a human or an AI. Despite mo

arXiv:2502.07617v2 Announce Type: replace Abstract: We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We fi

arXiv:2606.00664v1 Announce Type: cross Abstract: Embodied world models have emerged as a promising paradigm in robotics by predicting how robot actions affect the surrounding scene. However, the roll

arXiv:2606.02374v1 Announce Type: new Abstract: Earth Observation (EO) has fundamentally transformed the monitoring of environmental processes and human activities up to planetary scale. Recent advanc

arXiv:2606.00851v1 Announce Type: cross Abstract: Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or a

arXiv:2606.01106v1 Announce Type: new Abstract: TimeLogicQA evaluates whether video question answering systems can reason over temporal relations such as event existence, ordering, persistence, bounda

arXiv:2606.01599v1 Announce Type: new Abstract: Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals. Existing visual RL post-training trains

arXiv:2511.21397v2 Announce Type: replace-cross Abstract: How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior work on text-only languag

arXiv:2509.25773v3 Announce Type: replace-cross Abstract: AI models capable of comprehending humor hold real-world promise -- for example, enhancing engagement in human-machine interactions. To gauge

arXiv:2606.01955v1 Announce Type: cross Abstract: WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining

arXiv:2606.01936v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have opened up new possibilities for automated document formatting. However, real-world formatting often

arXiv:2606.00046v1 Announce Type: cross Abstract: Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as

arXiv:2603.07751v2 Announce Type: replace-cross Abstract: Current Large Language Models have achieved Olympiad-level logic, yet Vision-Language Models paradoxically falter on elementary spatial tasks

arXiv:2605.30510v1 Announce Type: cross Abstract: Brain cancer's severity necessitates precise brain tumor segmentation, which is crucial for effective brain tumor diagnosis. Manual identification, bu

arXiv:2605.31174v1 Announce Type: new Abstract: Object detection in real-world scenarios remains challenging due to diverse image degradations and heterogeneous object distributions, which significant

arXiv:2605.31432v1 Announce Type: cross Abstract: Simultaneous speech-to-text translation (SimulST) generates translations while speech is still unfolding, requiring a streaming policy that decides wh

arXiv:2602.01173v3 Announce Type: replace Abstract: Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowerin

arXiv:2605.31349v1 Announce Type: cross Abstract: Hateful meme detection remains a formidable challenge for vision-language models, as existing benchmarks are structurally observational - confounding

arXiv:2605.31063v1 Announce Type: cross Abstract: Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformation

arXiv:2605.30818v1 Announce Type: cross Abstract: Non-contact material identification enables adaptive interaction for embodied intelligence yet faces challenges from geometry-induced variations (e.g.

arXiv:2605.31039v1 Announce Type: new Abstract: Real-world image restoration (IR) is bottlenecked by the scarcity of high-quality paired training data. Synthetic datasets are abundant but often fail t

arXiv:2605.30639v1 Announce Type: cross Abstract: Embodied agents have made strong progress in navigating to target objects, but reaching the goal vicinity does not guarantee that the agent has found

arXiv:2511.17185v2 Announce Type: replace Abstract: We propose PostCam, a streamlined framework for novel-view video generation that achieves superior detail preservation and precise camera trajectory

arXiv:2605.31589v1 Announce Type: new Abstract: While humans naturally gesture during speech, only a sparse subset of these movements are visually depictive and semantically linked to specific spoken

arXiv:2603.08385v2 Announce Type: replace-cross Abstract: Brain tumors result in 20 years of lost life on average. Standard therapies induce complex structural changes in the brain that are monitored

arXiv:2602.16682v2 Announce Type: replace Abstract: A core aspect of human perception is situated awareness, the ability to relate ourselves to the surrounding physical environment and reason over pos

arXiv:2605.30698v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on visual question answering (VQA). To mitigate individual hallucinations and blind spo

arXiv:2605.31597v1 Announce Type: new Abstract: Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-leve

arXiv:2602.22971v2 Announce Type: replace Abstract: As LLMs achieved breakthroughs in general reasoning, their proficiency in specialized scientific domains reveals pronounced gaps in existing benchma

arXiv:2605.31321v1 Announce Type: new Abstract: Diffusion-based imitation learning methods have driven rapid progress in robot dexterous manipulation tasks. However, they have limitations when applied

arXiv:2605.31529v1 Announce Type: new Abstract: True video intelligence demands more than recognizing what is visible: it requires reasoning about why events unfold, predicting what would change under

arXiv:2605.31069v1 Announce Type: cross Abstract: Accurately predicting future events is fundamental to content understanding and decision-making across various domains. While prior research has prima

arXiv:2605.31040v1 Announce Type: new Abstract: Developing effective representations for register transfer level (RTL) designs is crucial for accelerating the hardware design workflow. Existing approa

arXiv:2601.14855v3 Announce Type: replace Abstract: Black-box variational inference (BBVI) with Gaussian mixture families offers a flexible approach for approximating complex posterior distributions w

arXiv:2605.30140v1 Announce Type: new Abstract: Benefiting from generalizability of vision-language models (VLMs) such as CLIP, many zero-/few-shot anomaly detection (AD) approaches have achieved impr

arXiv:2602.08979v2 Announce Type: replace-cross Abstract: Audio chaptering, the task of segmenting long-form audio into coherent sections, is increasingly important for navigating podcasts, lectures,

arXiv:2605.29021v1 Announce Type: new Abstract: Active tether-net systems are a promising solution for capturing large non-cooperative targets, such as space debris, by deploying a flexible net manipu

arXiv:2605.29879v1 Announce Type: new Abstract: Integrating open-vocabulary semantic information into dynamic 3D scene representations is essential for long-term embodied scene understanding. However,

arXiv:2605.29927v1 Announce Type: cross Abstract: Despite recent advances, LLM-based web agents still struggle with limited exploration, omission of critical steps, and sensitivity to task constraints

arXiv:2510.27607v3 Announce Type: replace Abstract: Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predictin

arXiv:2605.30350v1 Announce Type: cross Abstract: Robot manipulation critically depends on perception that preserves the action-relevant aspects of a scene. Yet most robot learning pipelines are built

arXiv:2605.29074v1 Announce Type: new Abstract: Are current Vision Language Models (VLMs) ready to comprehend and reason about complex embodied interactions in 3D environments? We introduce Embodied3D

arXiv:2605.30062v1 Announce Type: new Abstract: The development of generative artificial intelligence technologies has propelled the visual realism of synthetic images to an unprecedented level. Altho

arXiv:2605.28995v1 Announce Type: new Abstract: Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end

arXiv:2605.30045v1 Announce Type: new Abstract: Video object removal frequently struggles to simultaneously eliminate target objects and their associated physical effects (e.g., smoke, reflections, li

arXiv:2410.15236v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling app