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Found 12 papers, 4 papers with code
Time-dependent Entity Embedding is not All You Need: A Re-evaluation of Temporal Knowledge Graph Completion Models under a Unified Framework
Various temporal knowledge graph (KG) completion models have been proposed in the recent literature.
Memory Helps, but Confabulation Misleads: Understanding Streaming Events in Videos with MLLMs
Multimodal large language models (MLLMs) have demonstrated strong performance in understanding videos holistically, yet their ability to process streaming videos-videos are treated as a sequence of visual events-remains underexplored.
Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries
This progress is largely driven by the effective alignment between visual data and the language space of MLLMs.
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
However, directly applying pretrained LDM to heterogeneous OSFL results in significant distribution shifts in synthetic data, leading to performance degradation in classification models trained on such data.
VideoINSTA: Zero-shot Long Video Understanding via Informative Spatial-Temporal Reasoning with LLMs
The challenge of information redundancy in long videos prompts the question of what specific information is essential for large language models (LLMs) and how to leverage them for complex spatial-temporal reasoning in long-form video analysis.
Multimodal Pragmatic Jailbreak on Text-to-image Models
This work introduces a novel type of jailbreak, which triggers T2I models to generate the image with visual text, where the image and the text, although considered to be safe in isolation, combine to form unsafe content.
Localizing Events in Videos with Multimodal Queries
Localizing events in videos based on semantic queries is a pivotal task in video understanding, with the growing significance of user-oriented applications like video search.
SPOT! Revisiting Video-Language Models for Event Understanding
This raises a question: with such weak supervision, can video representation in video-language models gain the ability to distinguish even factual discrepancies in textual description and understand fine-grained events?
Multi-event Video-Text Retrieval
In this study, we introduce the Multi-event Video-Text Retrieval (MeVTR) task, addressing scenarios in which each video contains multiple different events, as a niche scenario of the conventional Video-Text Retrieval Task.
A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
This paper aims to provide a comprehensive survey of cutting-edge research in prompt engineering on three types of vision-language models: multimodal-to-text generation models (e. g. Flamingo), image-text matching models (e. g.
Can Vision-Language Models be a Good Guesser? Exploring VLMs for Times and Location Reasoning
This makes us wonder if, based on visual cues, Vision-Language Models that are pre-trained with large-scale image-text resources can achieve and even outperform human's capability in reasoning times and location.
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Visual Question Answering (VQA) is a multi-discipline research task.
