arXiv:2605.07575v2 Announce Type: replace-cross
Abstract: Proactive streaming video understanding requires Video-LLMs to decide when to respond as a video unfolds, a task where existing methods often fall short due to their implicit, query-agnostic mo…
arXiv:2605.10496v1 Announce Type: new
Abstract: Tiny UAV detection from an onboard event camera is difficult when the observer and target move at the same time. In this motion-on-motion regime, ego-motion activates background edges across buildings, v…
arXiv:2601.22143v2 Announce Type: replace-cross
Abstract: Audio-Visual Foundation Models, which are pretrained to jointly generate sound and visual content, have recently shown an unprecedented ability to model multi-modal generation and editing, open…
arXiv:2605.08663v1 Announce Type: new
Abstract: We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the challenges of magnitude-only 60~GHz radar Range-Tim…
arXiv:2603.12800v2 Announce Type: replace-cross
Abstract: We propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus image…
arXiv:2605.10543v1 Announce Type: new
Abstract: Director-style prompting, robotic action prediction, and interactive video agents demand temporal grounding over concurrent events — a regime in which 68% of general clips and over 99% of robotics/gamep…
arXiv:2605.10588v1 Announce Type: new
Abstract: Current Large Multimodal Models (LMMs) struggle with spatial reasoning tasks requiring viewpoint-dependent understanding, largely because they are confined to a single, static observation. We propose Thi…
arXiv:2605.09417v1 Announce Type: new
Abstract: Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets while maintaining consistent identities across frames. However, most existing app…
arXiv:2605.10730v1 Announce Type: new
Abstract: We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing m…
arXiv:2605.10645v1 Announce Type: new
Abstract: Data-driven medical AI is traditionally formulated as a discriminative mapping from input $X$ to output $Y$ via a learned function $f$, which does not generalize well across heterogeneous data and modali…