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Found 8 papers, 3 papers with code
TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models
Our study of existing benchmarks shows that this capability of MFMs is likely overestimated as many questions can be solved by using a single, few, or out-of-order frames.
EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance
Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to effectively incorporate both sources of guidance.
The CLIP Model is Secretly an Image-to-Prompt Converter
The Stable Diffusion model is a prominent text-to-image generation model that relies on a text prompt as its input, which is encoded using the Contrastive Language-Image Pre-Training (CLIP).
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
Seeking Subjectivity in Visual Emotion Distribution Learning
In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory.
Don't Stop Learning: Towards Continual Learning for the CLIP Model
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
Stimuli-Aware Visual Emotion Analysis
Then, we design three specific networks, i. e., Global-Net, Semantic-Net and Expression-Net, to extract distinct emotional features from different stimuli simultaneously.
