2 citations · 2 across the 10 of their papers we have counts for
12 papers
UAOR: Uncertainty-aware Observation Reinjection for Vision-Language-Action Models
Jiabing Yang, Yixiang Chen, Yuan Xu +14
Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demonstrating remarkable potential for…
Diversity Has Always Been There in Your Visual Autoregressive Models
Tong Wang, Guanyu Yang, Nian Liu +6
Visual Autoregressive (VAR) models have recently garnered significant attention for their innovative next-scale prediction paradigm, offering notable advantages in both inference e…
Composed Object Retrieval: Object-level Retrieval via Composed Expressions
Tong Wang, Guanyu Yang, Nian Liu +4
Retrieving fine-grained visual content based on user intent remains a challenge in multimodal systems. Although current Composed Image Retrieval (CIR) methods combine reference ima…
AURORA:Augmented Understanding via Structured Reasoning and Reinforcement Learning for Reference Audio-Visual Segmentation
Ziyang Luo, Nian Liu, Fahad Shahbaz Khan +1
Reference Audio-Visual Segmentation (Ref-AVS) tasks challenge models to precisely locate sounding objects by integrating visual, auditory, and textual cues. Existing methods often…
RAGNet: Large-scale Reasoning-based Affordance Segmentation Benchmark towards General Grasping
Dongming Wu, Yanping Fu, Saike Huang +8
General robotic grasping systems require accurate object affordance perception in diverse open-world scenarios following human instructions. However, current studies suffer from th…
TAViS: Text-bridged Audio-Visual Segmentation with Foundation Models
Ziyang Luo, Nian Liu, Xuguang Yang +5
Audio-Visual Segmentation (AVS) faces a fundamental challenge of effectively aligning audio and visual modalities. While recent approaches leverage foundation models to address dat…
Unsupervised Learning for Class Distribution Mismatch
Pan Du, Wangbo Zhao, Xinai Lu +8
Class distribution mismatch (CDM) refers to the discrepancy between class distributions in training data and target tasks. Previous methods address this by designing classifiers to…
CONDA: Condensed Deep Association Learning for Co-Salient Object Detection
Long Li, Nian Liu, Dingwen Zhang +6
Inter-image association modeling is crucial for co-salient object detection. Despite satisfactory performance, previous methods still have limitations on sufficient inter-image ass…
Learning Camouflaged Object Detection from Noisy Pseudo Label
Jin Zhang, Ruiheng Zhang, Yanjiao Shi +3
Existing Camouflaged Object Detection (COD) methods rely heavily on large-scale pixel-annotated training sets, which are both time-consuming and labor-intensive. Although weakly su…
TransGOP: Transformer-Based Gaze Object Prediction
Binglu Wang, Chenxi Guo, Yang Jin +2
Gaze object prediction aims to predict the location and category of the object that is watched by a human. Previous gaze object prediction works use CNN-based object detectors to p…
V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with Vehicle to Vehicle Communication
Yuanfang Zhang, Junxuan Li, Kaiqing Luo +6
Semantic scene completion (SSC) has recently gained popularity because it can provide both semantic and geometric information that can be used directly for autonomous vehicle navig…
Towards Instance-adaptive Inference for Federated Learning
Chun-Mei Feng, Kai Yu, Nian Liu +3
Federated learning (FL) is a distributed learning paradigm that enables multiple clients to learn a powerful global model by aggregating local training. However, the performance of…