11 accepted papers
Multimodal Large Language Models (MLLMs) have shown promising progress in understanding and analyzing video content. However, processing long videos remains a significant challenge constrained by LLM's context size. To address this limitation, we propose \textbf{LongVU}, a spatiotemporal adaptive co…
Recent generative models have demonstrated impressive capabilities in generating realistic and visually pleasing images grounded on textual prompts. Nevertheless, a significant challenge remains in applying these models for the more intricate task of story visualization. Since it requires resolving…
Understanding and reasoning over long videos pose significant challenges for large video language models (LVLMs) due to the difficulty in processing intensive video tokens beyond context window and retaining long-term sequential information. Retrieval-Augmented Generation (RAG) has demonstrated effe…
Knowledge discovery and collection are intelligence-intensive tasks that traditionally require significant human effort to ensure high-quality outputs. Recent research has explored multi-agent frameworks for automating Wikipedia-style article generation by retrieving and synthesizing information fro…
Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based…
"We introduce Affective Visual Dialog, an emotion explanation and reasoning task as a testbed for research on understanding constructed emotions in response to visually grounded conversations. The task involves three skills: (1) Dialog-based Question Answering (2) Dialog-based Emotion Prediction and…
"Most current LLM-based models for video understanding can process videos within minutes. However, they struggle with lengthy videos due to challenges such as “noise and redundancy”, as well as “memory and computation” constraints. In this paper, we present , a methodology tailored for comprehending…
The recent GPT-4 has demonstrated extraordinary multi-modal abilities, such as directly generating websites from handwritten text and identifying humorous elements within images. These features are rarely observed in previous vision-language models. However, the technical details behind GPT-4 contin…
Designing robust text-to-image (T2I) models have been extensively explored in recent years, especially with the emergence of diffusion models, which achieves state-of-the-art results on T2I synthesis tasks. Despite the significant effort and success in this direction, we observed that the existing m…
Video generation remains a challenging task due to spatiotemporal complexity and the requirement of synthesizing diverse motions with temporal consistency. Previous works attempt to generate videos in arbitrary lengths either in an autoregressive manner or regarding time as a continuous signal. Howe…
"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. At this scale, especially with many fine-grained categories included in ImageNet-21K, it is crit…