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Found 7 papers, 4 papers with code
II-MMR: Identifying and Improving Multi-modal Multi-hop Reasoning in Visual Question Answering
II-MMR then analyzes this path to identify different reasoning cases in current VQA benchmarks by estimating how many hops and what types (i. e., visual or beyond-visual) of reasoning are required to answer the question.
Dual-View Visual Contextualization for Web Navigation
Automatic web navigation aims to build a web agent that can follow language instructions to execute complex and diverse tasks on real-world websites.
GPT-4V(ision) is a Generalist Web Agent, if Grounded
The recent development on large multimodal models (LMMs), especially GPT-4V(ision) and Gemini, has been quickly expanding the capability boundaries of multimodal models beyond traditional tasks like image captioning and visual question answering.
PreSTU: Pre-Training for Scene-Text Understanding
The ability to recognize and reason about text embedded in visual inputs is often lacking in vision-and-language (V&L) models, perhaps because V&L pre-training methods have often failed to include such an ability in their training objective.
One Step at a Time: Long-Horizon Vision-and-Language Navigation with Milestones
We study the problem of developing autonomous agents that can follow human instructions to infer and perform a sequence of actions to complete the underlying task.
Discovering the Unknown Knowns: Turning Implicit Knowledge in the Dataset into Explicit Training Examples for Visual Question Answering
We found that many of the "unknowns" to the learned VQA model are indeed "known" in the dataset implicitly.
Revisiting Document Representations for Large-Scale Zero-Shot Learning
Zero-shot learning aims to recognize unseen objects using their semantic representations.
