7 papers
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…
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…
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…