3VL: Using Trees to Improve Vision-Language Models' Interpretability

• URL: http://arxiv.org/abs/2312.17345v2
• Date: Wed, 15 Jan 2025 12:46:07 GMT
• Title: 3VL: Using Trees to Improve Vision-Language Models' Interpretability
• Authors: Nir Yellinek, Leonid Karlinsky, Raja Giryes,
• Abstract summary: Vision-Language models (VLMs) have proven to be effective at aligning image and text representations, producing superior zero-shot results when transferred to many downstream tasks.<n>These representations suffer from some key shortcomings in understanding Compositional Language Concepts (CLC), such as recognizing objects' attributes, states, and relations between different objects.<n>In this work, we introduce the architecture and training technique of Tree-augmented Vision-Language (3VL) model accompanied by our proposed Anchor inference method and Differential Relevance (DiRe) interpretability tool.
• Score: 40.678288227161936
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Vision-Language models (VLMs) have proven to be effective at aligning image and text representations, producing superior zero-shot results when transferred to many downstream tasks. However, these representations suffer from some key shortcomings in understanding Compositional Language Concepts (CLC), such as recognizing objects' attributes, states, and relations between different objects. Moreover, VLMs typically have poor interpretability, making it challenging to debug and mitigate compositional-understanding failures. In this work, we introduce the architecture and training technique of Tree-augmented Vision-Language (3VL) model accompanied by our proposed Anchor inference method and Differential Relevance (DiRe) interpretability tool. By expanding the text of an arbitrary image-text pair into a hierarchical tree structure using language analysis tools, 3VL allows the induction of this structure into the visual representation learned by the model, enhancing its interpretability and compositional reasoning. Additionally, we show how Anchor, a simple technique for text unification, can be used to filter nuisance factors while increasing CLC understanding performance, e.g., on the fundamental VL-Checklist benchmark. We also show how DiRe, which performs a differential comparison between VLM relevancy maps, enables us to generate compelling visualizations of the reasons for a model's success or failure. Our code is available at: https://github.com/niryellinek/3VL.

Related papers

• NAVER: A Neuro-Symbolic Compositional Automaton for Visual Grounding with Explicit Logic Reasoning [22.60247555240363]
  This paper explores challenges for methods that require reasoning like human cognition. We propose NAVER, a compositional visual grounding method that integrates explicit probabilistic logic reasoning. Our results show that NAVER achieves SoTA performance comparing to recent end-to-end and compositional baselines.
  arXiv  Detail & Related papers  (2025-02-01T09:19:08Z)
• Causal Graphical Models for Vision-Language Compositional Understanding [36.24185263818946]
  We show that our method significantly outperforms all the state-of-the-art compositional approaches by a large margin. It also improves over methods trained using much larger datasets.
  arXiv  Detail & Related papers  (2024-12-12T15:22:03Z)
• Language Model as Visual Explainer [72.88137795439407]
  We present a systematic approach for interpreting vision models using a tree-structured linguistic explanation. Our method provides human-understandable explanations in the form of attribute-laden trees. To access the effectiveness of our approach, we introduce new benchmarks and conduct rigorous evaluations.
  arXiv  Detail & Related papers  (2024-12-08T20:46:23Z)
• ComAlign: Compositional Alignment in Vision-Language Models [2.3250871476216814]
  We introduce Compositional Alignment (ComAlign) to discover more exact correspondence of text and image components. Our methodology emphasizes that the compositional structure extracted from the text modality must also be retained in the image modality. We train a lightweight network lying on top of existing visual and language encoders using a small dataset.
  arXiv  Detail & Related papers  (2024-09-12T16:46:41Z)
• Unified Lexical Representation for Interpretable Visual-Language Alignment [52.059812317944434]
  We introduce LexVLA, a framework for learning a unified lexical representation for both modalities without complex design. We use DINOv2 as our visual model for its local-inclined features and Llama 2, a generative language model, to leverage its in-context lexical prediction ability. We demonstrate that these two pre-trained uni-modal models can be well-aligned by fine-tuning on the modest multi-modal dataset.
  arXiv  Detail & Related papers  (2024-07-25T07:35:27Z)
• In-Context Learning Improves Compositional Understanding of Vision-Language Models [2.762909189433944]
  compositional image understanding remains a rather difficult task due to the object bias present in training data. We compare contrastive models with generative ones and analyze their differences in architecture, pre-training data, and training tasks and losses. Our proposed approach outperforms baseline models across multiple compositional understanding datasets.
  arXiv  Detail & Related papers  (2024-07-22T09:03:29Z)
• Weak-to-Strong Compositional Learning from Generative Models for Language-based Object Detection [19.610781457283966]
  We introduce a novel method for enhancing the compositional understanding of vision-language (VL) models in language-based object detection. Our framework generates densely paired positive and negative triplets (image, text descriptions, and bounding boxes) in both image and text domains. We propose a new compositional contrastive learning formulation that discovers semantics and structures in complex descriptions from synthetic triplets.
  arXiv  Detail & Related papers  (2024-07-21T23:43:24Z)
• Emergent Visual-Semantic Hierarchies in Image-Text Representations [13.300199242824934]
  We study the knowledge of existing foundation models, finding that they exhibit emergent understanding of visual-semantic hierarchies. We propose the Radial Embedding (RE) framework for probing and optimizing hierarchical understanding.
  arXiv  Detail & Related papers  (2024-07-11T14:09:42Z)
• Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models [73.40350756742231]
  Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning. Despite the volume of new releases, key design decisions around image preprocessing, architecture, and optimization are under-explored.
  arXiv  Detail & Related papers  (2024-02-12T18:21:14Z)
• Leveraging VLM-Based Pipelines to Annotate 3D Objects [68.51034848207355]
  We propose an alternative algorithm to marginalize over factors such as the viewpoint that affect the VLM's response. Instead of merging text-only responses, we utilize the VLM's joint image-text likelihoods. We show how a VLM-based pipeline can be leveraged to produce reliable annotations for 764K objects from the 764K dataset.
  arXiv  Detail & Related papers  (2023-11-29T17:54:22Z)
• Text encoders bottleneck compositionality in contrastive vision-language models [76.2406963762722]
  We train text-only recovery probes that aim to reconstruct captions from single-vector text representations. We find that CLIP's text encoder falls short on more compositional inputs. Results suggest text-only recoverability is a necessary (but not sufficient) condition for modeling compositional factors.
  arXiv  Detail & Related papers  (2023-05-24T08:48:44Z)
• Coarse-to-Fine Contrastive Learning in Image-Text-Graph Space for Improved Vision-Language Compositionality [50.48859793121308]
  Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning. Recent research has highlighted severe limitations in their ability to perform compositional reasoning over objects, attributes, and relations.
  arXiv  Detail & Related papers  (2023-05-23T08:28:38Z)
• Teaching Structured Vision&Language Concepts to Vision&Language Models [46.344585368641006]
  We introduce the collective notion of Structured Vision&Language Concepts (SVLC) SVLC includes object attributes, relations, and states which are present in the text and visible in the image. We propose a more elegant data-driven approach for enhancing VL models' understanding of SVLCs.
  arXiv  Detail & Related papers  (2022-11-21T18:54:10Z)
• Perceptual Grouping in Contrastive Vision-Language Models [59.1542019031645]
  We show how vision-language models are able to understand where objects reside within an image and group together visually related parts of the imagery. We propose a minimal set of modifications that results in models that uniquely learn both semantic and spatial information.
  arXiv  Detail & Related papers  (2022-10-18T17:01:35Z)
• Unsupervised Vision-Language Parsing: Seamlessly Bridging Visual Scene Graphs with Language Structures via Dependency Relationships [17.930724926012264]
  We introduce a new task that targets on inducing a joint vision-language structure in an unsupervised manner. Our goal is to bridge the visual scene graphs and linguistic dependency trees seamlessly. We propose an automatic alignment procedure to produce coarse structures followed by human refinement to produce high-quality ones.
  arXiv  Detail & Related papers  (2022-03-27T09:51:34Z)
• Object Relational Graph with Teacher-Recommended Learning for Video Captioning [92.48299156867664]
  We propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model.
  arXiv  Detail & Related papers  (2020-02-26T15:34:52Z)

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.