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Found 16 papers, 8 papers with code
Robust Synthetic-to-Real Transfer for Stereo Matching
With advancements in domain generalized stereo matching networks, models pre-trained on synthetic data demonstrate strong robustness to unseen domains.
Multi-Tower Multi-Interest Recommendation with User Representation Repel
In the era of information overload, the value of recommender systems has been profoundly recognized in academia and industry alike.
RA-Rec: An Efficient ID Representation Alignment Framework for LLM-based Recommendation
To address this limitation, we propose a new paradigm, ID representation, which incorporates pre-trained ID embeddings into LLMs in a complementary manner.
The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture
We also trained state-of-the-art ML models on this dataset to approximate calculations at the DFT level.
Research on Patch Attentive Neural Process
Attentive Neural Process (ANP) improves the fitting ability of Neural Process (NP) and improves its prediction accuracy, but the higher time complexity of the model imposes a limitation on the length of the input sequence.
A Compositional Feature Embedding and Similarity Metric for Ultra-Fine-Grained Visual Categorization
Motivated by these issues, this paper proposes a novel compositional feature embedding and similarity metric (CECS).
Mask-Guided Feature Extraction and Augmentation for Ultra-Fine-Grained Visual Categorization
The advantage of the proposed method is that the feature detection and extraction model only requires a small amount of target region samples with bounding boxes for training, then it can automatically locate the target area for a large number of images in the dataset at a high detection accuracy.
PGTRNet: Two-phase Weakly Supervised Object Detection with Pseudo Ground Truth Refinement
There are two main problems hindering the performance of the two-phase WSOD approaches, i. e., insufficient learning problem and strict reliance between the FSD and the pseudo ground truth (PGT) generated by the WSOD model.
Feature Fusion Vision Transformer for Fine-Grained Visual Categorization
We verify the effectiveness of FFVT on three benchmarks where FFVT achieves the state-of-the-art performance.
Ranked #6 on
Fine-Grained Image Classification
on CUB-200-2011
Fine-Grained Image Classification
Fine-Grained Visual Categorization
EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation
The precise detection of blood vessels in retinal images is crucial to the early diagnosis of the retinal vascular diseases, e. g., diabetic, hypertensive and solar retinopathies.
Mask Guided Attention For Fine-Grained Patchy Image Classification
Specifically, the proposed MGA integrates a pre-trained semantic segmentation model that produces auxiliary supervision signal, i. e., patchy attention mask, enabling a discriminative representation learning.
Benchmark Platform for Ultra-Fine-Grained Visual Categorization Beyond Human Performance
The proposed UFG image dataset and evaluation protocols is intended to serve as a benchmark platform that can advance research of visual classification from approaching human performance to beyond human ability, via facilitating benchmark data of artificial intelligence (AI) not to be limited by the labels of human intelligence (HI).
Towards Context-Aware Code Comment Generation
Code comments are vital for software maintenance and comprehension, but many software projects suffer from the lack of meaningful and up-to-date comments in practice.
Patchy Image Structure Classification Using Multi-Orientation Region Transform
To address above limitations, this paper proposes a novel Multi-Orientation Region Transform (MORT), which can effectively characterize both contour and structure features simultaneously, for patchy image structure classification.
From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images
Encouraging experimental results of the proposed method in comparison to the state-of-the-art leaf species recognition methods demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.
Lattice CNNs for Matching Based Chinese Question Answering
Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly.
