The promise of such language model crossover (which is simple to implement and can leverage many different open-source language models) is that it enables a simple mechanism to evolve semantically-rich text representations (with few domain-specific tweaks), and naturally benefits from current progress in language models.
The objective of this paper is motion segmentation -- discovering and segmenting the moving objects in a video.
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
Ranked #9 on Atari Games on Atari 2600 Star Gunner
Answering real-world user queries, such as product search, often requires accurate retrieval of information from semi-structured knowledge bases or databases that involve blend of unstructured (e. g., textual descriptions of products) and structured (e. g., entity relations of products) information.
A key insight is to encode the 3D Gaussian attributes into a shared implicit feature representation, where it is merged with audio features to manipulate each Gaussian attribute.
PLLaVA achieves new state-of-the-art performance on modern benchmark datasets for both video question-answer and captioning tasks.
Ranked #1 on Zero-Shot Video Question Answer on TGIF-QA
Dense Captioning Video-based Generative Performance Benchmarking (Consistency) +6
The evaluation of large language models (LLMs) is crucial to assess their performance and mitigate potential security risks.
However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length.
We introduce UFO, an innovative UI-Focused agent to fulfill user requests tailored to applications on Windows OS, harnessing the capabilities of GPT-Vision.
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.