42 citations · 214 across the 65 of their papers we have counts for
59 papers · 1 filter
Robust Vision-Language Navigation with Awareness of Instruction-Execution Progress
Jiagan Huang, Zikun Zhou, Hongpeng Wang +3
In Vision-Language Navigation (VLN) tasks involving multi-step instructions as guidance, an agent must associate its observations with the relevant instruction content at each deci…
Restoring without Forgetting: Filter-Level Continual Image Restoration via Parameter-Space Integrated Gradients
Xin Feng, Jin Zhao, Yizhen Zhang +3
Adapting image restoration models to a stream of new tasks without revisiting past data remains challenging due to catastrophic forgetting. In this work, we propose Restoring witho…
RCoT-Seg: Reinforced Chain-of-Thought for Video Reasoning and Segmentation
Junwei Wen, Deshui Miao, Guangming Lu +2
Video Reasoning Segmentation (VRS) aims to segment target objects in videos based on implicit instructions that convey human intent and temporal logic. Existing MLLM-based methods…
Amodal SAM: A Unified Amodal Segmentation Framework with Generalization
Bo Zhang, Zhuotao Tian, Xin Tao +3
Amodal segmentation is a challenging task that aims to predict the complete geometric shape of objects, including their occluded regions. Although existing methods primarily focus…
Video-ToC: Video Tree-of-Cue Reasoning
Qizhong Tan, Zhuotao Tian, Guangming Lu +2
Existing Video Large Language Models (Video LLMs) struggle with complex video understanding, exhibiting limited reasoning capabilities and potential hallucinations. In particular,…
Too Vivid to Be Real? Benchmarking and Calibrating Generative Color Fidelity
Zhengyao Fang, Zexi Jia, Yijia Zhong +5
Recent advances in text-to-image (T2I) generation have greatly improved visual quality, yet producing images that appear visually authentic to real-world photography remains challe…
Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
Zhengyao Fang, Pengyuan Lyu, Chengquan Zhang +3
Vision-language models (VLMs) face significant computational inefficiencies caused by excessive generation of visual tokens. While prior work shows that a large fraction of visual…
DiffTrans: Differentiable Geometry-Materials Decomposition for Reconstructing Transparent Objects
Changpu Li, Shuang Wu, Songlin Tang +3
Reconstructing transparent objects from a set of multi-view images is a challenging task due to the complicated nature and indeterminate behavior of light propagation. Typical meth…
EditInfinity: Image Editing with Binary-Quantized Generative Models
Jiahuan Wang, Yuxin Chen, Jun Yu +2
Adapting pretrained diffusion-based generative models for text-driven image editing with negligible tuning overhead has demonstrated remarkable potential. A classical adaptation pa…
Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation
Zhenhua Ning, Zhuotao Tian, Shaoshuai Shi +4
Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). Ho…
Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval
Yushuai Sun, Zikun Zhou, Dongmei Jiang +4
Asymmetric retrieval is a typical scenario in real-world retrieval systems, where compatible models of varying capacities are deployed on platforms with different resource configur…
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…
Recognition-Synergistic Scene Text Editing
Zhengyao Fang, Pengyuan Lyu, Jingjing Wu +4
Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and conte…
UniVoxel: Fast Inverse Rendering by Unified Voxelization of Scene Representation
Shuang Wu, Songlin Tang, Guangming Lu +2
Typical inverse rendering methods focus on learning implicit neural scene representations by modeling the geometry, materials and illumination separately, which entails significant…
WeCromCL: Weakly Supervised Cross-Modality Contrastive Learning for Transcription-only Supervised Text Spotting
Jingjing Wu, Zhengyao Fang, Pengyuan Lyu +4
Transcription-only Supervised Text Spotting aims to learn text spotters relying only on transcriptions but no text boundaries for supervision, thus eliminating expensive boundary a…
Domain-Rectifying Adapter for Cross-Domain Few-Shot Segmentation
Jiapeng Su, Qi Fan, Guangming Lu +2
Few-shot semantic segmentation (FSS) has achieved great success on segmenting objects of novel classes, supported by only a few annotated samples. However, existing FSS methods oft…
Robust 3D Tracking with Quality-Aware Shape Completion
Jingwen Zhang, Zikun Zhou, Guangming Lu +2
3D single object tracking remains a challenging problem due to the sparsity and incompleteness of the point clouds. Existing algorithms attempt to address the challenges in two str…
STA-VPT: SpatioTemporally Aligned Visual Prompt Tuning
Wenjie Pei, Tongqi Xia, Qizhong Tan +3
Typical methods for visual prompt tuning follow the sequential modeling paradigm originating from NLP, learning a sequence of unordered parameterized tokens as visual prompts, whic…
DST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
Wenjie Pei, Qizhong Tan, Guangming Lu +2
Adapting pre-trained image models to video modality has proven to be an effective strategy for robust few-shot action recognition. In this work, we explore the potential of adapter…
Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection
Xin Feng, Yifeng Xu, Guangming Lu +1
Effective image restoration with large-size corruptions, such as blind image inpainting, entails precise detection of corruption region masks which remains extremely challenging du…
Scene-Generalizable Interactive Segmentation of Radiance Fields
Songlin Tang, Wenjie Pei, Xin Tao +3
Existing methods for interactive segmentation in radiance fields entail scene-specific optimization and thus cannot generalize across different scenes, which greatly limits their a…
Feature Decoupling-Recycling Network for Fast Interactive Segmentation
Huimin Zeng, Weinong Wang, Xin Tao +3
Recent interactive segmentation methods iteratively take source image, user guidance and previously predicted mask as the input without considering the invariant nature of the sour…
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided Enhancement
Zhenhua Ning, Zhuotao Tian, Guangming Lu +1
Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper pr…
Reliability-Hierarchical Memory Network for Scribble-Supervised Video Object Segmentation
Zikun Zhou, Kaige Mao, Wenjie Pei +3
This paper aims to solve the video object segmentation (VOS) task in a scribble-supervised manner, in which VOS models are not only trained by the sparse scribble annotations but a…