Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Led to

• Bottom-Up Top-Down attention2017 · cited 2×
  “Problems combining image and language understanding such as image captioning Chen2015 and visual question answering (VQA) balanced_vqa_v2 continue to inspire considerable research at the boundary of computer vision and n…”

  From Bottom-Up Top-Down attention · §Introduction
• Bilinear Attention Networks2018 · cited 2×
  “Finally, we validate our proposed method on a large and highly-competitive dataset, VQA 2.0 [8].”

  From Bilinear Attention Networks · §Introduction
• NLVR22018 · cited 2×
  “This relatively simple reasoning, together with biases in the data, removes much of the need to consider language compositionality Goyal et al. 2017.”

  From NLVR2 · §Introduction
• Multi-task hierarchical VL2018 · cited 4×
  “Since the recent successes of deep learning on single modality tasks, multi-modal tasks lying on the intersection of vision and language, such as image captioning mscoco; Young_2014_TACL, visual question answering (VQA)…”

  From Multi-task hierarchical VL · §Introduction
• VisualBERT2019 · cited 4×
  “We evaluate VisualBERT on four different types of vision-and-language applications: (1) Visual Question Answering (VQA 2.0) (Goyal et al. 2017), (2) Visual Commonsense Reasoning (VCR) (Zellers et al. 2019), (3) Natural L…”

  From VisualBERT · §Experiment
• LXMERT2019 · cited 1×, 1 in Method
  “We use three datasets for evaluating our LXMERT framework: VQA v2.0 dataset Goyal et al. 2017, GQA Hudson and Manning 2019, and NLVR2NLVR^{2}.”

  From LXMERT · §Experimental Setup and Results
• VL-BERT2019 · cited 3×
  “Here we conduct experiments on the widely-used VQA v2.0 dataset (Goyal et al. 2017), which is built based on the COCO (Lin et al. 2014) images.”

  From VL-BERT · §Experiment
• Decoupled box proposals captioning2019 · cited 2×, 1 in Method
  “Other VQA datasets, including VQA1.0 Antol et al. 2015, VQA2.0 Goyal et al. 2017, Visual7W Zhu et al. 2016, COCOQA Ren et al. 2015a, and GQA Hudson and Manning 2019 are completely or partly based on MSCOCO or Visual Geno…”

  From Decoupled box proposals captioning · §Visual Question Answering
• Grid features for VQA2020 · cited 4×
  “It is also worth noting that while region features are effective on benchmarks like VQA antol2015vqa; goyal2017making and COCO captions chen2015microsoft, for benchmarks that diagnose a model’s reasoning abilities when a…”

  From Grid features for VQA · §Related Work
• VILLA2020 · cited 2×
  “When finetuned on downstream tasks, these pre-trained models have achieved state-of-the-art performance across diverse V+L tasks, such as Visual Question Answering (VQA) [4, 17], Visual Commonsense Reasoning (VCR) [81],…”

  From VILLA · §Introduction
• VL-BERT meta-analysis2020 · cited 2×, 1 in Method
  “We consider the most common tasks used to evaluate V&L BERTs, spanning four groups: vocab-based VQA (Goyal et al. 2017; Hudson and Manning 2019), image–text retrieval (Lin et al. 2014; Plummer et al. 2015), referring exp…”

  From VL-BERT meta-analysis · §Experimental Setup
• VinVL2021 · cited 3×, 1 in Method
  “We build our pre-training corpus based on three types of existing vision and VL datasets: (1) image captioning datasets with human-annotated captions as 𝒘\boldsymbol{w} and machine-generated 55 5 We use the same model t…”

  From VinVL · §Oscar+ Pre-training
• VL-T52021 · cited 7×, 3 in Method
  “Following this success, image+text pretraining models (Lu et al. 2019; Tan & Bansal 2019; Chen et al. 2020; Huang et al. 2020; Li et al. 2020b; Cho et al. 2020; Radford et al. 2021; Zhang et al. 2021) and video+text pret…”

  From VL-T5 · §Related Works
• Conceptual 12M2021 · cited 2×
  “Second, many of the small-sized datasets share the same, limited visual domain; COCO-Captions [20], Visual Genome [44], and VQA2 [27] are (mostly) based on several hundreds thousand of COCO images [52].”

  From Conceptual 12M · §Introduction
• SimVLM2021 · cited 2×
  “A line of work (Tan & Bansal 2019; Lu et al. 2019; Li et al. 2019; Chen et al. 2020b; Li et al. 2020; Su et al. 2020; Zhang et al. 2021) has explored vision-language pretraining (VLP) that learns a joint representation o…”

  From SimVLM · §Introduction
• VLMo2021 · cited 2×
  “We train and evaluate the model on VQA 2.0 dataset [15].”

  From VLMo · §Experiments
• Florence2021 · cited 2×, 1 in Method
  “To evaluate the performance, we fine-tune the pre-trained model on the challenging VQA (Goyal et al. 2017) task, which is to answer a question based on the image context.”

  From Florence · §Experiments
• VL-BEiT2022 · cited 2×
  “Following previous work [16, 41], we use VQA 2.0 dataset [10], and formulate the task as a classification problem to choose the answer from 3,1293,129 most frequent answers.”

  From VL-BEiT · §Experiments
• BEiT-32022 · cited 2×
  “Following previous work [2, 65, 26], we conduct finetuning experiments on the VQA v2.0 dataset [19] and formulate the task as a classification problem.”

  From BEiT-3 · §Experiments on Vision and Vision-Language Tasks