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Found 10 papers, 6 papers with code
Synthesize Step-by-Step: Tools, Templates and LLMs as Data Generators for Reasoning-Based Chart VQA
In particular, our approach improves the accuracy of the previous state-of-the-art approach from 38% to 54% on the human-written questions in the ChartQA dataset, which needs strong reasoning.
Causal-CoG: A Causal-Effect Look at Context Generation for Boosting Multi-modal Language Models
We propose Causal Context Generation, Causal-CoG, which is a prompting strategy that engages contextual information to enhance precise VQA during inference.
3D-Aware Visual Question Answering about Parts, Poses and Occlusions
In this work, we introduce the task of 3D-aware VQA, which focuses on challenging questions that require a compositional reasoning over the 3D structure of visual scenes.
Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models
Despite the impressive advancements achieved through vision-and-language pretraining, it remains unclear whether this joint learning paradigm can help understand each individual modality.
Super-CLEVR: A Virtual Benchmark to Diagnose Domain Robustness in Visual Reasoning
Visual Question Answering (VQA) models often perform poorly on out-of-distribution data and struggle on domain generalization.
Visual Commonsense in Pretrained Unimodal and Multimodal Models
Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple.
Ranked #1 on
Visual Commonsense Tests
on ViComTe-color
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering
By swapping the context object features, the model reliance on context can be suppressed effectively.
Calibrating Concepts and Operations: Towards Symbolic Reasoning on Real Images
Our experiments show CCO substantially boosts the performance of neural symbolic methods on real images.
Context-Aware Group Captioning via Self-Attention and Contrastive Features
In this paper, we introduce a new task, context-aware group captioning, which aims to describe a group of target images in the context of another group of related reference images.
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Our proposed FD-GAN achieves state-of-the-art performance on three person reID datasets, which demonstrates that the effectiveness and robust feature distilling capability of the proposed FD-GAN.
Ranked #3 on
Person Re-Identification
on CUHK03
