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Found 6 papers, 3 papers with code
Honeybee: Locality-enhanced Projector for Multimodal LLM
In Multimodal Large Language Models (MLLMs), a visual projector plays a crucial role in bridging pre-trained vision encoders with LLMs, enabling profound visual understanding while harnessing the LLMs' robust capabilities.
Ranked #1 on
Science Question Answering
on ScienceQA
(using extra training data)
Large Language Models are Temporal and Causal Reasoners for Video Question Answering
We observe that the LLMs provide effective priors in exploiting $\textit{linguistic shortcuts}$ for temporal and causal reasoning in Video Question Answering (VideoQA).
Ranked #1 on
Video Question Answering
on TVQA
NICE: CVPR 2023 Challenge on Zero-shot Image Captioning
In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge.
Open-Vocabulary Object Detection using Pseudo Caption Labels
Recent open-vocabulary detection methods aim to detect novel objects by distilling knowledge from vision-language models (VLMs) trained on a vast amount of image-text pairs.
Noise-aware Learning from Web-crawled Image-Text Data for Image Captioning
Image captioning is one of the straightforward tasks that can take advantage of large-scale web-crawled data which provides rich knowledge about the visual world for a captioning model.
Dense but Efficient VideoQA for Intricate Compositional Reasoning
It is well known that most of the conventional video question answering (VideoQA) datasets consist of easy questions requiring simple reasoning processes.
