arXiv subjects

Kevin Dela Rosa

Publications and source records attributed to Kevin Dela Rosa.

8 recordsLinked to original sources

arXiv2026

Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness

A production agent harness must discover and rank, from a growing library of skills, the one most appropriate for a user's task. At small scale this selection happens in context: the LLM planner chooses among skill representations exposed in its system prompt, without an explicit embedding-based retrieval step. We treat this in-context selection as the small-N counterpart to embedding-based skill retrieval at scale, and present a case study of how Tinycloud, a production multimodal video agent harness, represents its skills for the planner. The harness ships skills under two recurring representations: tool-skills that wrap a single external API or system tool and serve as primitive vocabulary, and workflow-skills that orchestrate tool-skill calls plus a template render to produce one named deliverable. The harness exposes them via two surfaces in the system prompt: an inlined-body surface (full instructions, scripts, templates) for autoloaded skills, and a one-line listing for on-demand skills. A six-task selection ablation across three exposure regimes (all-on, default, all-off) shows that full autoload selects the gold skill on every task; all-off slows execution and produces hard discovery failures; and the production default misroutes one task because its lexical signal collides with an autoloaded tool-skill that pulls planner attention away from a listed workflow-skill. The headline finding is that in-prompt exposure of skills is not monotonically helpful: partial exposure can create lexical competition that suppresses correct selection. We connect this small-N observation to recent retrieval-based skill-routing work at large scale, and frame this contribution as a case study rather than a benchmark.

cs.AI↗

arXiv2025

Smart Routing for Multimodal Video Retrieval: When to Search What

We introduce ModaRoute, an LLM-based intelligent routing system that dynamically selects optimal modalities for multimodal video retrieval. While dense text captions can achieve 75.9% Recall@5, they require expensive offline processing and miss critical visual information present in 34% of clips with scene text not captured by ASR. By analyzing query intent and predicting information needs, ModaRoute reduces computational overhead by 41% while achieving 60.9% Recall@5. Our approach uses GPT-4.1 to route queries across ASR (speech), OCR (text), and visual indices, averaging 1.78 modalities per query versus exhaustive 3.0 modality search. Evaluation on 1.8M video clips demonstrates that intelligent routing provides a practical solution for scaling multimodal retrieval systems, reducing infrastructure costs while maintaining competitive effectiveness for real-world deployment.

cs.CV↗

arXiv2025

RAVEN: An Agentic Framework for Multimodal Entity Discovery from Large-Scale Video Collections

We present RAVEN an adaptive AI agent framework designed for multimodal entity discovery and retrieval in large-scale video collections. Synthesizing information across visual, audio, and textual modalities, RAVEN autonomously processes video data to produce structured, actionable representations for downstream tasks. Key contributions include (1) a category understanding step to infer video themes and general-purpose entities, (2) a schema generation mechanism that dynamically defines domain-specific entities and attributes, and (3) a rich entity extraction process that leverages semantic retrieval and schema-guided prompting. RAVEN is designed to be model-agnostic, allowing the integration of different vision-language models (VLMs) and large language models (LLMs) based on application-specific requirements. This flexibility supports diverse applications in personalized search, content discovery, and scalable information retrieval, enabling practical applications across vast datasets.

cs.IR↗

arXiv2024

Video Enriched Retrieval Augmented Generation Using Aligned Video Captions

In this work, we propose the use of "aligned visual captions" as a mechanism for integrating information contained within videos into retrieval augmented generation (RAG) based chat assistant systems. These captions are able to describe the visual and audio content of videos in a large corpus while having the advantage of being in a textual format that is both easy to reason about & incorporate into large language model (LLM) prompts, but also typically require less multimedia content to be inserted into the multimodal LLM context window, where typical configurations can aggressively fill up the context window by sampling video frames from the source video. Furthermore, visual captions can be adapted to specific use cases by prompting the original foundational model / captioner for particular visual details or fine tuning. In hopes of helping advancing progress in this area, we curate a dataset and describe automatic evaluation procedures on common RAG tasks.

cs.AI↗

arXiv2023

Multimodality-guided Image Style Transfer using Cross-modal GAN Inversion

Image Style Transfer (IST) is an interdisciplinary topic of computer vision and art that continuously attracts researchers' interests. Different from traditional Image-guided Image Style Transfer (IIST) methods that require a style reference image as input to define the desired style, recent works start to tackle the problem in a text-guided manner, i.e., Text-guided Image Style Transfer (TIST). Compared to IIST, such approaches provide more flexibility with text-specified styles, which are useful in scenarios where the style is hard to define with reference images. Unfortunately, many TIST approaches produce undesirable artifacts in the transferred images. To address this issue, we present a novel method to achieve much improved style transfer based on text guidance. Meanwhile, to offer more flexibility than IIST and TIST, our method allows style inputs from multiple sources and modalities, enabling MultiModality-guided Image Style Transfer (MMIST). Specifically, we realize MMIST with a novel cross-modal GAN inversion method, which generates style representations consistent with specified styles. Such style representations facilitate style transfer and in principle generalize any IIST methods to MMIST. Large-scale experiments and user studies demonstrate that our method achieves state-of-the-art performance on TIST task. Furthermore, comprehensive qualitative results confirm the effectiveness of our method on MMIST task and cross-modal style interpolation.

cs.CV↗

arXiv2023

FORB: A Flat Object Retrieval Benchmark for Universal Image Embedding

Image retrieval is a fundamental task in computer vision. Despite recent advances in this field, many techniques have been evaluated on a limited number of domains, with a small number of instance categories. Notably, most existing works only consider domains like 3D landmarks, making it difficult to generalize the conclusions made by these works to other domains, e.g., logo and other 2D flat objects. To bridge this gap, we introduce a new dataset for benchmarking visual search methods on flat images with diverse patterns. Our flat object retrieval benchmark (FORB) supplements the commonly adopted 3D object domain, and more importantly, it serves as a testbed for assessing the image embedding quality on out-of-distribution domains. In this benchmark we investigate the retrieval accuracy of representative methods in terms of candidate ranks, as well as matching score margin, a viewpoint which is largely ignored by many works. Our experiments not only highlight the challenges and rich heterogeneity of FORB, but also reveal the hidden properties of different retrieval strategies. The proposed benchmark is a growing project and we expect to expand in both quantity and variety of objects. The dataset and supporting codes are available at https://github.com/pxiangwu/FORB/.

cs.CV↗

arXiv2021

Open-Vocabulary Object Detection Using Captions

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more categories with less supervision, but they have not been as successful and widely adopted as supervised models. In this paper, we put forth a novel formulation of the object detection problem, namely open-vocabulary object detection, which is more general, more practical, and more effective than weakly supervised and zero-shot approaches. We propose a new method to train object detectors using bounding box annotations for a limited set of object categories, as well as image-caption pairs that cover a larger variety of objects at a significantly lower cost. We show that the proposed method can detect and localize objects for which no bounding box annotation is provided during training, at a significantly higher accuracy than zero-shot approaches. Meanwhile, objects with bounding box annotation can be detected almost as accurately as supervised methods, which is significantly better than weakly supervised baselines. Accordingly, we establish a new state of the art for scalable object detection.

cs.CV↗

arXiv2010

The Advection of Supergranules by the Sun's Axisymmetric Flows

We show that the motions of supergranules are consistent with a model in which they are simply advected by the axisymmetric flows in the Sun's surface shear layer. We produce a 10-day series of simulated Doppler images at a 15-minute cadence that reproduces most spatial and temporal characteristics seen in the SOHO/MDI Doppler data. Our simulated data have a spectrum of cellular flows with just two components -- a granule component that peaks at spherical wavenumbers of about 4000 and a supergranule component that peaks at wavenumbers of about 110. We include the advection of these cellular components by the axisymmetric flows -- differential rotation and meridional flow -- whose variations with latitude and depth (wavenumber) are consistent with observations. We mimic the evolution of the cellular pattern by introducing random variations to the phases of the spectral components at rates that reproduce the levels of cross-correlation as functions of time and latitude. Our simulated data do not include any wave-like characteristics for the supergranules yet can reproduce the rotation characteristics previously attributed to wave-like behavior. We find rotation rates which appear faster than the actual rotation rates and attribute this to the projection effects. We find that the measured meridional flow does accurately represent the actual flow and that the observations indicate poleward flow to $65\degr-70\degr$ latitude with equatorward counter cells in the polar regions.

astro-ph.SR↗