5 papers
IDEAL: In-DEpth ALignment Makes A Discrete Representation AutoEncoder
Yitong Chen, Zijie Diao, Junke Wang +5
Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing semantically rich latent spac…
OccVLA: Vision-Language-Action Model with Implicit 3D Occupancy Supervision
Ruixun Liu, Lingyu Kong, Derun Li +1
Multimodal large language models (MLLMs) have shown strong vision-language reasoning abilities but still lack robust 3D spatial understanding, which is critical for autonomous driv…
Clapper: Compact Learning and Video Representation in VLMs
Lingyu Kong, Hongzhi Zhang, Jingyuan Zhang +4
Current vision-language models (VLMs) have demonstrated remarkable capabilities across diverse video understanding applications. Designing VLMs for video inputs requires effectivel…
Pix2Cap-COCO: Advancing Visual Comprehension via Pixel-Level Captioning
Zuyao You, Junke Wang, Lingyu Kong +2
We present Pix2Cap-COCO, the first panoptic pixel-level caption dataset designed to advance fine-grained visual understanding. To achieve this, we carefully design an automated ann…
FOCUS: Towards Universal Foreground Segmentation
Zuyao You, Lingyu Kong, Lingchen Meng +1
Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for e…