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Found 9 papers, 7 papers with code
MiniGPT4-Video: Advancing Multimodal LLMs for Video Understanding with Interleaved Visual-Textual Tokens
This paper introduces MiniGPT4-Video, a multimodal Large Language Model (LLM) designed specifically for video understanding.
StoryGPT-V: Large Language Models as Consistent Story Visualizers
Therefore, we introduce \textbf{StoryGPT-V}, which leverages the merits of the latent diffusion (LDM) and LLM to produce images with consistent and high-quality characters grounded on given story descriptions.
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Motivated by this, we target to build a unified interface for completing many vision-language tasks including image description, visual question answering, and visual grounding, among others.
Ranked #10 on
Visual Question Answering
on BenchLMM
Affective Visual Dialog: A Large-Scale Benchmark for Emotional Reasoning Based on Visually Grounded Conversations
We introduce Affective Visual Dialog, an emotion explanation and reasoning task as a testbed for research on understanding the formation of emotions in visually grounded conversations.
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
Our work, for the first time, uncovers that properly aligning the visual features with an advanced large language model can possess numerous advanced multi-modal abilities demonstrated by GPT-4, such as detailed image description generation and website creation from hand-drawn drafts.
Ranked #9 on
Visual Question Answering
on BenchLMM
HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models
A human evaluation aligned with 95% of our evaluations on average was conducted to probe the effectiveness of HRS-Bench.
MoStGAN-V: Video Generation with Temporal Motion Styles
Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency.
ChatGPT Asks, BLIP-2 Answers: Automatic Questioning Towards Enriched Visual Descriptions
By keeping acquiring new visual information from BLIP-2's answers, ChatCaptioner is able to generate more enriched image descriptions.
Exploring Hierarchical Graph Representation for Large-Scale Zero-Shot Image Classification
The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the ImageNet-21K benchmark.
