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Found 12 papers, 8 papers with code
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
Enhancing reasoning in Large Multimodal Models (LMMs) faces unique challenges from the complex interplay between visual perception and logical reasoning, particularly in compact 3B-parameter architectures where architectural constraints limit reasoning capacity and modality alignment.
Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution
This loss captures intra-dataset relationships, facilitating co-training across multiple IQA datasets.
UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion
In this work, we propose UltraFusion, the first exposure fusion technique that can merge input with 9 stops differences.
SAIL: Sample-Centric In-Context Learning for Document Information Extraction
Nonetheless, training-free methods for DIE encounter two primary challenges: (1) understanding the complex relationship between layout and textual elements in VRDs, and (2) providing accurate guidance to pre-trained models.
An Intelligent Agentic System for Complex Image Restoration Problems
Real-world image restoration (IR) is inherently complex and often requires combining multiple specialized models to address diverse degradations.
PhoCoLens: Photorealistic and Consistent Reconstruction in Lensless Imaging
By conditioning on the low-frequency content retrieved in the first stage, the diffusion model effectively reconstructs the high-frequency details that are typically lost in the lensless imaging process, while also maintaining image fidelity.
Interpreting Low-level Vision Models with Causal Effect Maps
Based on the causal effect theory, the proposed diagnostic tool can refresh our common knowledge and bring a deeper understanding of low-level vision models.
Descriptive Image Quality Assessment in the Wild
We introduce a ground-truth-informed dataset construction approach to enhance data quality, and scale up the dataset to 495K under the brief-detail joint framework.
Depicting Beyond Scores: Advancing Image Quality Assessment through Multi-modal Language Models
To build the DepictQA model, we establish a hierarchical task framework, and collect a multi-modal IQA training dataset.
ADTR: Anomaly Detection Transformer with Feature Reconstruction
Second, CNN tends to reconstruct both normal samples and anomalies well, making them still hard to distinguish.
A Unified Model for Multi-class Anomaly Detection
For example, when learning a unified model for 15 categories in MVTec-AD, we surpass the second competitor on the tasks of both anomaly detection (from 88. 1% to 96. 5%) and anomaly localization (from 89. 5% to 96. 8%).
Ranked #13 on
Multi-class Anomaly Detection
on MVTec AD
Few-shot Object Counting with Similarity-Aware Feature Enhancement
This work studies the problem of few-shot object counting, which counts the number of exemplar objects (i. e., described by one or several support images) occurring in the query image.
Ranked #2 on
Object Counting
on CARPK
