13 citations · 15 across the 16 of their papers we have counts for
19 papers
Domain Adaptive Object Detection via Dual-Stream Bilevel-Cycle Optimization
Yannan Chen, Wei Wang, Wenqiang Wang +5
Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain adaptation and exploits unlabe…
CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering
Qiming Li, Zekai Ye, Xiaocheng Feng +9
Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, l…
Interactive Tracking: A Human-in-the-Loop Paradigm with Memory-Augmented Adaptation
Yuqing Huang, Guotian Zeng, Zhenqiao Yuan +4
Existing visual trackers mainly operate in a non-interactive, fire-and-forget manner, making them impractical for real-world scenarios that require human-in-the-loop adaptation. To…
Cluster-Wise Spatio-Temporal Masking for Efficient Video-Language Pretraining
Weijun Zhuang, Yuqing Huang, Weikang Meng +5
Large-scale video-language pretraining enables strong generalization across multimodal tasks but often incurs prohibitive computational costs. Although recent advances in masked vi…
CLIP-Guided Adaptable Self-Supervised Learning for Human-Centric Visual Tasks
Mingshuang Luo, Ruibing Hou, Bo Chao +4
Human-centric visual analysis plays a pivotal role in diverse applications, including surveillance, healthcare, and human-computer interaction. With the emergence of large-scale un…
MCAT: Scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 Languages
Yexing Du, Kaiyuan Liu, Youcheng Pan +7
Multimodal Large Language Models (MLLMs) have achieved great success in Speech-to-Text Translation (S2TT) tasks. However, current research is constrained by two key challenges: lan…
Did Models Learn Sufficiently? Attribution-Guided Training via Subset-Selected Counterfactual Augmentation
Yannan Chen, Ruoyu Chen, Wei Wang +6
Current visual models often make predictions based on a limited set of discriminative visual cues. As a result, they may become unreliable when the distribution shifts or when thes…
VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRI
Chenqian Le, Yilin Zhao, Nikasadat Emami +4
Recent advances in fMRI-based visual decoding have enabled compelling reconstructions of perceived images. However, most approaches rely on subject-specific training, limiting scal…
CAI: Caption-Sensitive Attention Intervention for Mitigating Object Hallucination in Large Vision-Language Models
Qiming Li, Zekai Ye, Xiaocheng Feng +8
Although Large Vision-Language Models (LVLMs) have demonstrated powerful capabilities in interpreting visual information, they frequently produce content that deviates from visual…
NormDirection: Restoring the Missing Query Norm in Vision Linear Attention
Weikang Meng, Yadan Luo, Liangyu Huo +4
Linear attention mitigates the quadratic complexity of softmax attention but suffers from a critical loss of expressiveness. We identify two primary causes: (1) The normalization o…
FOCUS: Unified Vision-Language Modeling for Interactive Editing Driven by Referential Segmentation
Fan Yang, Yousong Zhu, Xin Li +6
Recent Large Vision Language Models (LVLMs) demonstrate promising capabilities in unifying visual understanding and generative modeling, enabling both accurate content understandin…
Prototype Perturbation for Relaxing Alignment Constraints in Backward-Compatible Learning
Zikun Zhou, Yushuai Sun, Wenjie Pei +2
The traditional paradigm to update retrieval models requires re-computing the embeddings of the gallery data, a time-consuming and computationally intensive process known as backfi…
LSVOS Challenge Report: Large-scale Complex and Long Video Object Segmentation
Henghui Ding, Lingyi Hong, Chang Liu +30
Despite the promising performance of current video segmentation models on existing benchmarks, these models still struggle with complex scenes. In this paper, we introduce the 6th…
Discriminative Spatial-Semantic VOS Solution: 1st Place Solution for 6th LSVOS
Deshui Miao, Yameng Gu, Xin Li +3
Video object segmentation (VOS) is a crucial task in computer vision, but current VOS methods struggle with complex scenes and prolonged object motions. To address these challenges…
Learning Spatial-Semantic Features for Robust Video Object Segmentation
Xin Li, Deshui Miao, Zhenyu He +3
Tracking and segmenting multiple similar objects with distinct or complex parts in long-term videos is particularly challenging due to the ambiguity in identifying target component…
PVUW 2024 Challenge on Complex Video Understanding: Methods and Results
Henghui Ding, Chang Liu, Yunchao Wei +34
Pixel-level Video Understanding in the Wild Challenge (PVUW) focus on complex video understanding. In this CVPR 2024 workshop, we add two new tracks, Complex Video Object Segmentat…
1st Place Solution for MOSE Track in CVPR 2024 PVUW Workshop: Complex Video Object Segmentation
Deshui Miao, Xin Li, Zhenyu He +2
Tracking and segmenting multiple objects in complex scenes has always been a challenge in the field of video object segmentation, especially in scenarios where objects are occluded…
Harnessing Vision-Language Pretrained Models with Temporal-Aware Adaptation for Referring Video Object Segmentation
Zikun Zhou, Wentao Xiong, Li Zhou +3
The crux of Referring Video Object Segmentation (RVOS) lies in modeling dense text-video relations to associate abstract linguistic concepts with dynamic visual contents at pixel-l…
Learning Mask-aware CLIP Representations for Zero-Shot Segmentation
Siyu Jiao, Yunchao Wei, Yaowei Wang +2
Recently, pre-trained vision-language models have been increasingly used to tackle the challenging zero-shot segmentation task. Typical solutions follow the paradigm of first gener…