42 citations · 214 across the 65 of their papers we have counts for
70 papers
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…
Lifelong small-object navigation in changing object layouts: a benchmark and method
Jiagan Huang, Zikun Zhou, Zijian Ni +4
Household robots need to continually navigate to different objects in the same environment, many of which are small and portable, such as tools and toys. Their small visual footpri…
MoRA: MoE Pruning via Router Bias Learning and Expert Approximation
Yushuai Sun, Zikun Zhou, Lin Gao +2
Mixture-of-Experts (MoE) models enable parameter scaling with limited per-token computation by activating only a small subset of experts for each token, but deploying them still re…
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…
MISA: Mixture of Indexer Sparse Attention for Long-Context LLM Inference
Ruijie Zhou, Fanxu Meng, Yufei Xu +4
DeepSeek Sparse Attention (DSA) sets the state of the art for fine-grained inference-time sparse attention by introducing a learned token-wise indexer that scores every prefix toke…
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…
HISA: Efficient Hierarchical Indexing for Fine-Grained Sparse Attention
Yufei Xu, Fanxu Meng, Fan Jiang +11
Token-level sparse attention mechanisms, exemplified by DeepSeek Sparse Attention (DSA), achieve fine-grained key selection by scoring every historical key for each query through a…
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…
Breaking the Blocks: Continuous Low-Rank Decomposed Scaling for Unified LLM Quantization and Adaptation
Pingzhi Tang, Ruijie Zhou, Fanxu Meng +2
Current quantization methods for LLMs predominantly rely on block-wise structures to maintain efficiency, often at the cost of representational flexibility. In this work, we demons…
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…
Lightweight Joint Audio-Visual Deepfake Detection via Single-Stream Multi-Modal Learning Framework
Kuiyuan Zhang, Wenjie Pei, Rushi Lan +2
Deepfakes are AI-synthesized multimedia data that may be abused for spreading misinformation. Deepfake generation involves both visual and audio manipulation. To detect audio-visua…
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…
Saliency-Aware Regularized Graph Neural Network
Wenjie Pei, Weina Xu, Zongze Wu +4
The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when agg…
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…