Conditional image-to-video (cI2V) generation aims to synthesize a new plausible video
starting from an image (eg, a person's face) and a condition (eg, an action class label like …

In this paper, we study teacher-student learning from the perspective of data initialization
and propose a novel algorithm called Active Teacher for semi-supervised object detection …

With the advent of large language models (LLMs) enhanced by the chain-of-thought (CoT)
methodology, the visual reasoning problem is usually decomposed into manageable sub …

To bridge the ever-increasing gap between deep neural networks' complexity and hardware
capability, network quantization has attracted more and more research attention. The latest …

Post-training quantization (PTQ) is widely regarded as one of the most efficient compression
methods practically, benefitting from its data privacy and low computation costs. We argue …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

Researchers and practitioners have recently reframed powerful Large Language Models
(LLMs) as agents, enabling them to automate complex tasks largely via the use of …

Coreset selection is powerful in reducing computational costs and accelerating data
processing for deep learning algorithms. It strives to identify a small subset from large-scale …

In this work, we propose a hyperparameter optimization method named HyperTime to find
hyperparameters robust to potential temporal distribution shifts in the unseen test data. Our …

We present AutoGen, an open-source framework that allows developers to build LLM
applications by composing multiple agents to converse with each other to accomplish tasks …