29 citations · 32 across the 12 of their papers we have counts for
14 papers
Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models
Guoming Ling, Yupei Lin, Zhongzhan Huang +5
Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchm…
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models
Guoming Ling, Zhongzhan Huang, Yupei Lin +4
Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models generate reasoning steps sequentially…
Robust Egocentric Referring Video Object Segmentation via Dual-Modal Causal Intervention
Haijing Liu, Zhiyuan Song, Hefeng Wu +3
Egocentric Referring Video Object Segmentation (Ego-RVOS) aims to segment the specific object actively involved in a human action, as described by a language query, within first-pe…
DART: Dual Adaptive Refinement Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +2
Open-Vocabulary Multi-Label Recognition (OV-MLR) aims to identify multiple seen and unseen object categories within an image, requiring both precise intra-class localization to pin…
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
Zhongzhan Huang, Guoming Ling, Shanshan Zhong +2
Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existi…
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
Zhongzhan Huang, Guoming Ling, Yupei Lin +4
Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input. In this paper, our comprehensive an…
Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition
Haijing Liu, Tao Pu, Hefeng Wu +4
Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a wide range of visual concepts in…
Improving Network Interpretability via Explanation Consistency Evaluation
Hefeng Wu, Hao Jiang, Keze Wang +3
While deep neural networks have achieved remarkable performance, they tend to lack transparency in prediction. The pursuit of greater interpretability in neural networks often resu…
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning
Weifeng Chen, Jiacheng Zhang, Jie Wu +3
The rapid development of diffusion models has triggered diverse applications. Identity-preserving text-to-image generation (ID-T2I) particularly has received significant attention…
Dual-View Data Hallucination with Semantic Relation Guidance for Few-Shot Image Recognition
Hefeng Wu, Guangzhi Ye, Ziyang Zhou +3
Learning to recognize novel concepts from just a few image samples is very challenging as the learned model is easily overfitted on the few data and results in poor generalizabilit…
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search
Hefeng Wu, Weifeng Chen, Zhibin Liu +3
Given a descriptive text query, text-based person search (TBPS) aims to retrieve the best-matched target person from an image gallery. Such a cross-modal retrieval task is quite ch…
SQLNet: Scale-Modulated Query and Localization Network for Few-Shot Class-Agnostic Counting
Hefeng Wu, Yandong Chen, Lingbo Liu +3
The class-agnostic counting (CAC) task has recently been proposed to solve the problem of counting all objects of an arbitrary class with several exemplars given in the input image…
Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning
Weifeng Chen, Yatai Ji, Jie Wu +6
Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video g…
Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Tao Pu, Tianshui Chen, Hefeng Wu +1
Training the multi-label image recognition models with partial labels, in which merely some labels are known while others are unknown for each image, is a considerably challenging…