Is 'Right' Right? Enhancing Object Orientation Understanding in Multimodal Large Language Models through Egocentric Instruction Tuning

• URL: http://arxiv.org/abs/2411.16761v2
• Date: Sat, 29 Mar 2025 09:24:00 GMT
• Title: Is 'Right' Right? Enhancing Object Orientation Understanding in Multimodal Large Language Models through Egocentric Instruction Tuning
• Authors: Ji Hyeok Jung, Eun Tae Kim, Seoyeon Kim, Joo Ho Lee, Bumsoo Kim, Buru Chang,
• Abstract summary: Multimodal large language models (MLLMs) act as essential interfaces, connecting humans with AI technologies in multimodal applications.<n>Current MLLMs face challenges in accurately interpreting object orientation in images due to inconsistent orientation annotations in training data.<n>We propose egocentric instruction tuning, which aligns MLLMs' orientation understanding with the user's perspective.
• Score: 7.911608620021529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal large language models (MLLMs) act as essential interfaces, connecting humans with AI technologies in multimodal applications. However, current MLLMs face challenges in accurately interpreting object orientation in images due to inconsistent orientation annotations in training data, hindering the development of a coherent orientation understanding. To overcome this, we propose egocentric instruction tuning, which aligns MLLMs' orientation understanding with the user's perspective, based on a consistent annotation standard derived from the user's egocentric viewpoint. We first generate egocentric instruction data that leverages MLLMs' ability to recognize object details and applies prior knowledge for orientation understanding. Using this data, we perform instruction tuning to enhance the model's capability for accurate orientation interpretation. In addition, we introduce EgoOrientBench, a benchmark that evaluates MLLMs' orientation understanding across three tasks using images collected from diverse domains. Experimental results on this benchmark show that egocentric instruction tuning significantly improves orientation understanding without compromising overall MLLM performance. The instruction data and benchmark dataset are available on our project page at https://github.com/jhCOR/EgoOrientBench.

Related papers

• Empowering Reliable Visual-Centric Instruction Following in MLLMs [30.078761482873745]
  Existing benchmarks for evaluating MLLMs' instruction-following capability primarily focus on verbal instructions.<n>We introduce VC-IFEval, a new benchmark accompanied by a systematically constructed dataset.<n>Our benchmark systematically incorporates vision-dependent constraints into instruction design, enabling a more rigorous and fine-grained assessment.
  arXiv  Detail & Related papers  (2026-01-06T17:23:33Z)
• VLN-MME: Diagnosing MLLMs as Language-guided Visual Navigation agents [12.383467162169703]
  We introduce a unified and evaluation framework to probe MLLMs as zero-shot agents.<n>We simplify the evaluation with a highly modular and accessible design.<n>We observe that enhancing our baseline agent with Chain-of-Thought (CoT) reasoning and self-language leads to an unexpected performance decrease.
  arXiv  Detail & Related papers  (2025-12-31T13:21:21Z)
• Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models [53.06230963851451]
  JARVIS is a JEPA-inspired framework for self-supervised visual enhancement in MLLMs.<n>We introduce JARVIS, a JEPA-inspired framework for self-supervised visual enhancement in MLLMs.
  arXiv  Detail & Related papers  (2025-12-17T19:01:34Z)
• EgoInstruct: An Egocentric Video Dataset of Face-to-face Instructional Interactions with Multi-modal LLM Benchmarking [20.1877648578032]
  We present a new egocentric video dataset of face-to-face instruction.<n>We provide ground-truth annotations for two fundamental tasks that serve as a first step toward a comprehensive understanding of instructional interactions.<n>We evaluate recently introduced MLLMs that jointly process images, audio, and text.
  arXiv  Detail & Related papers  (2025-09-26T07:52:26Z)
• Structuring GUI Elements through Vision Language Models: Towards Action Space Generation [43.932266242034025]
  Multimodal large language models (MLLMs) have emerged as pivotal tools in enhancing human-computer interaction.<n>This paper focuses on the application of MLLMs in the field of graphical user interface (GUI) elements structuring.<n>We introduce an IoU-Augmented Maximum Likelihood (IAML) training paradigm to bolster visual module capabilities.
  arXiv  Detail & Related papers  (2025-08-22T10:14:15Z)
• Exo2Ego: Exocentric Knowledge Guided MLLM for Egocentric Video Understanding [69.96199605596138]
  Current MLLMs primarily focus on third-person (exocentric) vision, overlooking the unique aspects of first-person (egocentric) videos. We propose learning the mapping between exocentric and egocentric domains to enhance egocentric video understanding. We introduce Ego-ExoClip, a pre-training dataset comprising 1.1M synchronized ego-exo clip-text pairs.
  arXiv  Detail & Related papers  (2025-03-12T08:10:33Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment. By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs. Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• SoFar: Language-Grounded Orientation Bridges Spatial Reasoning and Object Manipulation [49.858348469657784]
  We introduce the concept of semantic orientation, which defines object orientations using natural language in a reference-frame-free manner. By integrating semantic orientation into a VLM system, we enable robots to generate manipulation actions with both positional and orientational constraints.
  arXiv  Detail & Related papers  (2025-02-18T18:59:02Z)
• Elevating Visual Perception in Multimodal LLMs with Visual Embedding Distillation [109.5893580175657]
  In recent times, the standard practice for developing MLLMs is to feed features from vision encoder(s) into the LLM and train with natural language supervision.<n>This approach often causes models to lean towards language comprehension and undermine the rich visual perception signals present in the data.<n>We propose VisPer-LM, the first approach that infuses visual perception knowledge from expert vision encoders into the LLM's hidden representations.
  arXiv  Detail & Related papers  (2024-12-12T18:55:18Z)
• EZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection [21.091101582856183]
  We introduce a novel prompt learning-based framework for Efficient Zero-Shot HOI detection (EZ-HOI). First, we introduce Large Language Model (LLM) and VLM guidance for learnable prompts, integrating detailed HOI descriptions and visual semantics to adapt VLMs to HOI tasks. We show that our framework achieves state-of-the-art performance across various zero-shot settings with only 10.35% to 33.95% of the trainable parameters compared to existing methods.
  arXiv  Detail & Related papers  (2024-10-31T13:06:29Z)
• Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
  multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets. However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs. This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
  arXiv  Detail & Related papers  (2024-07-05T17:43:30Z)
• Can LLMs Generate Human-Like Wayfinding Instructions? Towards Platform-Agnostic Embodied Instruction Synthesis [51.04181562775778]
  We present a novel approach to automatically synthesize "wayfinding instructions" for an embodied robot agent. Our algorithm uses in-context learning to condition an LLM to generate instructions using just a few references. We implement our approach on multiple simulation platforms including Matterport3D, AI Habitat and ThreeDWorld.
  arXiv  Detail & Related papers  (2024-03-18T05:38:07Z)
• RecExplainer: Aligning Large Language Models for Explaining Recommendation Models [50.74181089742969]
  Large language models (LLMs) have demonstrated remarkable intelligence in understanding, reasoning, and instruction following. This paper presents the initial exploration of using LLMs as surrogate models to explain black-box recommender models. To facilitate an effective alignment, we introduce three methods: behavior alignment, intention alignment, and hybrid alignment.
  arXiv  Detail & Related papers  (2023-11-18T03:05:43Z)
• Pink: Unveiling the Power of Referential Comprehension for Multi-modal LLMs [49.88461345825586]
  This paper proposes a new framework to enhance the fine-grained image understanding abilities of MLLMs. We present a new method for constructing the instruction tuning dataset at a low cost by leveraging annotations in existing datasets. We show that our model exhibits a 5.2% accuracy improvement over Qwen-VL and surpasses the accuracy of Kosmos-2 by 24.7%.
  arXiv  Detail & Related papers  (2023-10-01T05:53:15Z)
• From Language Modeling to Instruction Following: Understanding the Behavior Shift in LLMs after Instruction Tuning [63.63840740526497]
  We investigate how instruction tuning adjusts pre-trained models with a focus on intrinsic changes. The impact of instruction tuning is then studied by comparing the explanations derived from the pre-trained and instruction-tuned models. Our findings reveal three significant impacts of instruction tuning.
  arXiv  Detail & Related papers  (2023-09-30T21:16:05Z)
• MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models [73.86954509967416]
  Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks. This paper presents the first comprehensive MLLM Evaluation benchmark MME. It measures both perception and cognition abilities on a total of 14 subtasks.
  arXiv  Detail & Related papers  (2023-06-23T09:22:36Z)

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.