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Found 8 papers, 7 papers with code
Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
It builds the correspondence between image region proposal and query in an adaptive manner: adaptive grounding and collaborative reconstruction.
Knowledge-guided Pairwise Reconstruction Network for Weakly Supervised Referring Expression Grounding
Weakly supervised referring expression grounding (REG) aims at localizing the referential entity in an image according to linguistic query, where the mapping between the image region (proposal) and the query is unknown in the training stage.
Parsing-based View-aware Embedding Network for Vehicle Re-Identification
Vehicle Re-Identification is to find images of the same vehicle from various views in the cross-camera scenario.
Entity-enhanced Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding
Second, most previous weakly supervised REG methods ignore the discriminative location and context of the referent, causing difficulties in distinguishing the target from other same-category objects.
Deeply Coupled Cross-Modal Prompt Learning
Recent advancements in multimodal foundation models (e. g., CLIP) have excelled in zero-shot generalization.
What Large Language Models Bring to Text-rich VQA?
This pipeline achieved superior performance compared to the majority of existing Multimodal Large Language Models (MLLM) on four text-rich VQA datasets.
Context Disentangling and Prototype Inheriting for Robust Visual Grounding
In this paper, we propose a novel framework with context disentangling and prototype inheriting for robust visual grounding to handle both scenes.
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs).
