Revisiting KRISP: A Lightweight Reproduction and Analysis of Knowledge-Enhanced Vision-Language Models

• URL: http://arxiv.org/abs/2511.20795v1
• Date: Tue, 25 Nov 2025 19:37:19 GMT
• Title: Revisiting KRISP: A Lightweight Reproduction and Analysis of Knowledge-Enhanced Vision-Language Models
• Authors: Souradeep Dutta, Keshav Bulia, Neena S Nair,
• Abstract summary: Facebook AI Research introduced KRISP, which integrates structured external knowledge into pipelines for vision-language reasoning.<n>Despite its effectiveness, the original model has been developed for industrial-scale training, is computationally demanding, and is tightly connected to a large backbone.<n>In this work, we reexamine KRISP from a different angle and offer a lightweight reproduction with significantly fewer parameters.
• Score: 1.873444918172383
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facebook AI Research introduced KRISP [4], which integrates structured external knowledge into pipelines for vision-language reasoning. Despite its effectiveness, the original model has been developed for industrial-scale training, is computationally demanding, and is tightly connected to a large backbone. In this work, we reexamine KRISP from a different angle and offer a lightweight reproduction with significantly fewer parameters. Even though our replicated model performs about 75 % of the original, the replication process uncovers a number of design flaws, real-world pitfalls, and implicit problems that were not fully covered in the original paper. We offer insights into the scalability and efficacy of knowledge-enhanced VQA architectures under resource constraints through systematic ablation studies, which include a proof-of-concept on synthetic VQA data and evaluation on the DAQUAR dataset. Our model, configured with a low parameter setup and constrained by the external Knowledge graph domain, prevents AI hallucinations and generates outputs solely within that domain. Minimal parameters allow us to function on edge devices like smartphones and AR-VR, further improving offline visual reasoning.

Related papers

• Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study [50.15623093332659]
  Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR)<n>We compare existing techniques across diverse settings and present aggregated performance results on multiple image quality metrics.<n>We examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training.
  arXiv  Detail & Related papers  (2026-01-25T07:09:20Z)
• Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models [8.589209709453026]
  Quantization, particularly Post-Training Quantization (PTQ), offers an effective way to reduce model size and inference cost without retraining.<n>We present a benchmark of eight state-of-the-art (SOTA) PTQ methods applied to two leading edge-ASR model families, Whisper and Moonshine.<n>Our results characterize the trade-offs between efficiency and accuracy, demonstrating that even $3$-bit quantization can succeed on high capacity models.
  arXiv  Detail & Related papers  (2025-07-10T16:00:27Z)
• Shifting AI Efficiency From Model-Centric to Data-Centric Compression [67.45087283924732]
  We argue that the focus of research for AI is shifting from model-centric compression to data-centric compression.<n>Data-centric compression improves AI efficiency by directly compressing the volume of data processed during model training or inference.<n>Our work aims to provide a novel perspective on AI efficiency, synthesize existing efforts, and catalyze innovation to address the challenges posed by ever-increasing context lengths.
  arXiv  Detail & Related papers  (2025-05-25T13:51:17Z)
• Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge [3.1471494780647795]
  Recent trends show a growing focus on compact models-typically under 10 billion parameters-enabled by techniques such as quantization.<n>This shift paves the way for LMs on edge devices, offering potential benefits such as enhanced privacy, reduced latency, and improved data sovereignty.<n>We present a comprehensive evaluation of generative LM inference on representative CPU-based and GPU-accelerated edge devices.
  arXiv  Detail & Related papers  (2025-03-12T07:01:34Z)
• KBAlign: Efficient Self Adaptation on Specific Knowledge Bases [73.34893326181046]
  We present KBAlign, a self-supervised framework that enhances RAG systems through efficient model adaptation.<n>Our key insight is to leverage the model's intrinsic capabilities for knowledge alignment through two innovative mechanisms.<n> Experiments demonstrate that KBAlign can achieve 90% of the performance gain obtained through GPT-4-supervised adaptation.
  arXiv  Detail & Related papers  (2024-11-22T08:21:03Z)
• Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey [16.89460694470542]
  Implicit Neural Representations (INRs) have emerged as a paradigm in knowledge representation.<n>INRs leverage multilayer perceptrons (MLPs) to model data as continuous implicit functions.<n>This survey introduces a clear taxonomy that categorises them into four key areas: activation functions, position encoding, combined strategies, and network structure.
  arXiv  Detail & Related papers  (2024-11-06T06:14:24Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
  Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness. Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings. This paper introduces an innovative multi-modal prompt-based methodology for IQA.
  arXiv  Detail & Related papers  (2024-04-23T11:45:32Z)
• OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System [7.1083241462091165]
  We introduce an external modality-guided data mining framework, primarily rooted in optical character recognition (OCR), to extract statistical features from images. A key aspect of our approach is the alignment of external modality features, extracted using a single modality-aware model, with image features encoded by a convolutional neural network. Our methodology considerably boosts the recall rate of the defect detection model and maintains high robustness even in challenging scenarios.
  arXiv  Detail & Related papers  (2024-03-18T07:41:39Z)
• Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective [64.04617968947697]
  We introduce a novel data-model co-design perspective: to promote superior weight sparsity. Specifically, customized Visual Prompts are mounted to upgrade neural Network sparsification in our proposed VPNs framework.
  arXiv  Detail & Related papers  (2023-12-03T13:50:24Z)
• A survey on Variational Autoencoders from a GreenAI perspective [0.0]
  Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks. This article provides a comparative evaluation of some of the most successful, recent variations of VAEs.
  arXiv  Detail & Related papers  (2021-03-01T15:26:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.