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Pseudo 3D Perception Transformer with Multi-level Confidence Optimization for Visual Commonsense Reasoning

2023-01-30 23:43:28
Jian Zhu, Hanli Wang


A framework performing Visual Commonsense Reasoning(VCR) needs to choose an answer and further provide a rationale justifying based on the given image and question, where the image contains all the facts for reasoning and requires to be sufficiently understood. Previous methods use a detector applied on the image to obtain a set of visual objects without considering the exact positions of them in the scene, which is inadequate for properly understanding spatial and semantic relationships between objects. In addition, VCR samples are quite diverse, and parameters of the framework tend to be trained suboptimally based on mini-batches. To address above challenges, pseudo 3D perception Transformer with multi-level confidence optimization named PPTMCO is proposed for VCR in this paper. Specifically, image depth is introduced to represent pseudo 3-dimension(3D) positions of objects along with 2-dimension(2D) coordinates in the image and further enhance visual features. Then, considering that relationships between objects are influenced by depth, depth-aware Transformer is proposed to do attention mechanism guided by depth differences from answer words and objects to objects, where each word is tagged with pseudo depth value according to related objects. To better optimize parameters of the framework, a model parameter estimation method is further proposed to weightedly integrate parameters optimized by mini-batches based on multi-level reasoning confidence. Experiments on the benchmark VCR dataset demonstrate the proposed framework performs better against the state-of-the-art approaches.

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3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot