10 accepted papers
Prevailing autoregressive (AR) models for text-to-image generation either rely on heavy, computationally-intensive diffusion models to process continuous image tokens, or employ vector quantization (VQ) to obtain discrete tokens with quantization loss. In this paper, we push the autoregressive parad…
Personalized image generation holds great promise in assisting humans in everyday work and life due to its impressive function in creatively generating personalized content. However, current evaluations either are automated but misalign with humans or require human evaluations that are time-consumin…
Inspired by the success of DeepSeek-R1, we explore the potential of rule-based reinforcement learning (RL) in MLLM post-training for perception policy learning. While promising, our initial experiments reveal that incorporating a thinking process through RL does not consistently lead to performance…
Human-AI interactivity is a critical aspect that reflects the usability of Multimodal Large Language Models (MLLMs). However, existing end-to-end MLLMs only allow users to interact with them through language instructions, leading to the limitation of the interactive accuracy and efficiency. In this…
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative m…
Long-term temporal fusion is a crucial but often overlooked technique in camera-based Bird's-Eye-View (BEV) 3D perception. Existing methods are mostly in a parallel manner. While parallel fusion can benefit from long-term information, it suffers from increasing computational and memory overheads as
Efficiency is quite important for 3D lane detection while previous detectors are either computationally expensive or difficult for optimization. To bridge this gap, we propose a fully convolutional detector named GroupLane, which is simple, fast, and still maintains high detection precision. Specifi
"This paper presents , the first 3D Multimodal Large Language Model (LLM) designed for embodied interaction, exploring a universal 3D object understanding with 3D point clouds and languages. is built upon an improved 3D encoder by extending [?] to that benefits from multi-view image distillation for…
"Most Large Vision-Language Models (LVLMs) enjoy the same vision vocabulary, i.e., CLIP, for common vision tasks. However, for some special task that needs dense and fine-grained perception, the CLIP-style vocabulary may encounter low efficiency in tokenizing corresponding vision knowledge and even…
In current face recognition approaches with convolutional neural network (CNN), a pair of faces to compare are independently fed into the CNN for feature extraction. For both faces the same kernels are applied and hence the representation of a face stays fixed regardless of who it is compared with.…