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

December 28, 2023 · Entered Twilight · 🏛 IEEE Transactions on Image Processing

Repo contents: CLIP_COCO_finetune.py, CLIP_COCO_lora_finetune.py, CLIP_decision_tree.py, CLIP_linear_probe.py, Decision_Tree_CLIP_CC3M_lora_finetune.py, Decision_Tree_CLIP_CC3M_train.py, Decision_Tree_CLIP_COCO_hilaCAM_finetune_and_contrast.py, Decision_Tree_CLIP_coarse_nodes_train.py, Decision_Tree_CLIP_living_node_train.py, Decision_Tree_CLIP_train.py, Decision_Tree_and_contrastive_CC3M_CLIP_lora_finetune.py, Decision_Tree_and_contrastive_CC3M_MR_CLIP_lora_finetune.py, Decision_Tree_and_contrastive_CLIP_lora_80_prompts_finetune.py, Decision_Tree_and_contrastive_CLIP_lora_finetune.py, Decision_Tree_and_contrastive_CLIP_lora_no_crop_finetune.py, Decision_Tree_and_contrastive_COCO_MR_contrast_CLIP_lora_finetune.py, Decision_Tree_and_contrastive_COCO_nerratives_CLIP_lora_finetune.py, Decision_Tree_ensemble_and_contrastive_CLIP_lora_finetune.py, Decision_Tree_mix_and_contrastive_LoRA_finetune.py, ImageNet_class_labels_clustering.py, calc_num_attr_appearances.py, calc_num_attr_appearances_per_class.py, calculate_mutual_information.py, clip_linear_probe_test.py, clip_linear_probe_train.py, cola_crepe.py, create_CC3M_MR_CLIP_contrastive_weights.py, create_COCO_MR_CLIP_contrastive_weights.py, create_ImageNet_wordnet_hierarchy_tree.py, create_attribute_vocabulary.py, create_coarse_sentences.py, create_csv.py, create_nouns_vocabulary.py, create_verb_vocabulary.py, custom_decision_tree.py, custom_decision_tree_025.py, datasets.py, get_top_mutual_information_attributes.py, hilaCAM_lora, lora, perturb_anchor_cam.py, perturb_anchor_diff_cams.py, perturb_anchor_diff_cams_double_scores.py, perturb_anchor_diff_cross_cams.py, perturb_anchor_diff_text.py, perturb_anchor_high_score_posneg_cams_double_scores.py, perturb_anchor_plus_high_score_posneg_cams.py, perturb_anchor_plus_posneg_cams_double_scores.py, perturb_anchor_pos_neg_triple_scores.py, perturb_aro_negclip.py, perturb_average_cams.py, perturb_average_minus_anchor_cam.py, perturb_cam_by_score.py, perturb_diff_cam.py, perturb_diff_cam_by_score.py, perturb_diff_minus_anchor.py, perturb_diff_no_norm_cam_test.py, perturb_negative_minus_positive.py, perturb_positive_minus_negative.py, perturb_random_diff.py, perturb_separate_cams.py, perturb_separate_cams_double_scores.py, perturb_separate_cross_cams.py, perturb_separate_diff_cams.py, perturb_separate_diff_cams_double_scores.py, perturbation.py, visualize_perturbed_image.py, winoground.py

Authors Nir Yellinek, Leonid Karlinsky, Raja Giryes arXiv ID 2312.17345 Category cs.CV: Computer Vision Citations 7 Venue IEEE Transactions on Image Processing Repository https://github.com/niryellinek/3VL Last Checked 3 months ago

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.