Trending Research
Rethinking Interpretability in the Era of Large Language Models
csinva/imodelsX
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We highlight two emerging research priorities for LLM interpretation: using LLMs to directly analyze new datasets and to generate interactive explanations.
MemGPT: Towards LLMs as Operating Systems
Large language models (LLMs) have revolutionized AI, but are constrained by limited context windows, hindering their utility in tasks like extended conversations and document analysis.
PLAYER*: Enhancing LLM-based Multi-Agent Communication and Interaction in Murder Mystery Games
Recent advancements in Large Language Models (LLMs) have enhanced the efficacy of agent communication and social interactions.
OpenVoice: Versatile Instant Voice Cloning
The voice styles are not directly copied from and constrained by the style of the reference speaker.
ID-Animator: Zero-Shot Identity-Preserving Human Video Generation
Based on this pipeline, a random face reference training method is further devised to precisely capture the ID-relevant embeddings from reference images, thus improving the fidelity and generalization capacity of our model for ID-specific video generation.
TensorIR: An Abstraction for Automatic Tensorized Program Optimization
mlc-ai/web-llm
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Finally, we build an end-to-end framework on top of our abstraction to automatically optimize deep learning models for given tensor computation primitives.
Vision-based 3D occupancy prediction in autonomous driving: a review and outlook
In recent years, autonomous driving has garnered escalating attention for its potential to relieve drivers' burdens and improve driving safety.
Foundation Models for Video Understanding: A Survey
Additionally, we offer an in-depth performance analysis of these models for the 6 most common video tasks.
emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
alibaba-damo-academy/FunASR
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To the best of our knowledge, emotion2vec is the first universal representation model in various emotion-related tasks, filling a gap in the field.
ConsistentID: Portrait Generation with Multimodal Fine-Grained Identity Preserving
ConsistentID comprises two key components: a multimodal facial prompt generator that combines facial features, corresponding facial descriptions and the overall facial context to enhance precision in facial details, and an ID-preservation network optimized through the facial attention localization strategy, aimed at preserving ID consistency in facial regions.
