36 citations · 48 across the 13 of their papers we have counts for
13 papers
ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing
Yongqi An, Chang Lu, Kuan Zhu +5
Large language models (LLMs) face growing challenges in efficient generative inference due to the increasing memory demands of Key-Value (KV) caches, especially for long sequences.…
Improving Generalization in LLM Structured Pruning via Function-Aware Neuron Grouping
Tao Yu, Yongqi An, Kuan Zhu +3
Large Language Models (LLMs) demonstrate impressive performance across natural language tasks but incur substantial computational and storage costs due to their scale. Post-trainin…
UniFGVC: Universal Training-Free Few-Shot Fine-Grained Vision Classification via Attribute-Aware Multimodal Retrieval
Hongyu Guo, Xiangzhao Hao, Jiarui Guo +3
Few-shot fine-grained visual classification (FGVC) aims to discriminate between subtly different categories with very limited data. While Multimodal Large Language Models (MLLM) ex…
FOCUS: Fine-grained Optimization with Semantic Guided Understanding for Pedestrian Attributes Recognition
Hongyan An, Kuan Zhu, Xin He +4
Pedestrian attribute recognition (PAR) is a fundamental perception task in intelligent transportation and security. To tackle this fine-grained task, most existing methods focus on…
Referring Expression Instance Retrieval and A Strong End-to-End Baseline
Xiangzhao Hao, Kuan Zhu, Hongyu Guo +5
Using natural language to query visual information is a fundamental need in real-world applications. Text-Image Retrieval (TIR) retrieves a target image from a gallery based on an…
Monocular Lane Detection Based on Deep Learning: A Survey
Xin He, Haiyun Guo, Kuan Zhu +4
Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on them have de…
Cracking the Code of Hallucination in LVLMs with Vision-aware Head Divergence
Jinghan He, Kuan Zhu, Haiyun Guo +6
Large vision-language models (LVLMs) have made substantial progress in integrating large language models (LLMs) with visual inputs, enabling advanced multimodal reasoning. Despite…
SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models
Jinghan He, Haiyun Guo, Kuan Zhu +3
Continual learning (CL) is crucial for language models to dynamically adapt to the evolving real-world demands. To mitigate the catastrophic forgetting problem in CL, data replay h…
Continual Instruction Tuning for Large Multimodal Models
Jinghan He, Haiyun Guo, Ming Tang +1
Instruction tuning is now a widely adopted approach to aligning large multimodal models (LMMs) to follow human intent. It unifies the data format of vision-language tasks, enabling…
Plug-and-Play Pseudo Label Correction Network for Unsupervised Person Re-identification
Tianyi Yan, Kuan Zhu, Haiyun guo +3
Clustering-based methods, which alternate between the generation of pseudo labels and the optimization of the feature extraction network, play a dominant role in both unsupervised…
PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification
Kuan Zhu, Haiyun Guo, Tianyi Yan +3
In person re-identification (ReID), very recent researches have validated pre-training the models on unlabelled person images is much better than on ImageNet. However, these resear…
AAformer: Auto-Aligned Transformer for Person Re-Identification
Kuan Zhu, Haiyun Guo, Shiliang Zhang +4
In person re-identification (re-ID), extracting part-level features from person images has been verified to be crucial to offer fine-grained information. Most of the existing CNN-b…
Identity-Guided Human Semantic Parsing for Person Re-Identification
Kuan Zhu, Haiyun Guo, Zhiwei Liu +2
Existing alignment-based methods have to employ the pretrained human parsing models to achieve the pixel-level alignment, and cannot identify the personal belongings (e.g., backpac…