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Found 20 papers, 14 papers with code
ProxyCLIP: Proxy Attention Improves CLIP for Open-Vocabulary Segmentation
ProxyCLIP leverages the spatial feature correspondence from VFMs as a form of proxy attention to augment CLIP, thereby inheriting the VFMs' robust local consistency and maintaining CLIP's exceptional zero-shot transfer capacity.
Ranked #1 on
Unsupervised Semantic Segmentation with Language-image Pre-training
on PASCAL Context-60
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+2
ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference
Despite the success of large-scale pretrained Vision-Language Models (VLMs) especially CLIP in various open-vocabulary tasks, their application to semantic segmentation remains challenging, producing noisy segmentation maps with mis-segmented regions.
Open Vocabulary Semantic Segmentation
Open-Vocabulary Semantic Segmentation
+1
Towards Vision-Language Geo-Foundation Model: A Survey
Vision-Language Foundation Models (VLFMs) have made remarkable progress on various multimodal tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding.
A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder
A$^{2}$-MAE integrates an anchor-aware masking strategy and a geographic encoding module to comprehensively exploit the properties of RS images.
H2RSVLM: Towards Helpful and Honest Remote Sensing Large Vision Language Model
The generic large Vision-Language Models (VLMs) is rapidly developing, but still perform poorly in Remote Sensing (RS) domain, which is due to the unique and specialized nature of RS imagery and the comparatively limited spatial perception of current VLMs.
SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation
Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation.
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Deep co-training has been introduced to semi-supervised segmentation and achieves impressive results, yet few studies have explored the working mechanism behind it.
Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection
In this study, we dive deep into the inconsistency of pseudo targets in semi-supervised object detection (SSOD).
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
In this work, we revisit the weak-to-strong consistency framework, popularized by FixMatch from semi-supervised classification, where the prediction of a weakly perturbed image serves as supervision for its strongly perturbed version.
Semi-supervised Change Detection
Semi-supervised Medical Image Segmentation
+1
ViM: Out-Of-Distribution with Virtual-logit Matching
Most of the existing Out-Of-Distribution (OOD) detection algorithms depend on single input source: the feature, the logit, or the softmax probability.
Semantically Coherent Out-of-Distribution Detection
The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner, but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Progressive Representative Labeling for Deep Semi-Supervised Learning
In this paper, we propose to label only the most representative samples to expand the labeled set.
Webly Supervised Image Classification with Metadata: Automatic Noisy Label Correction via Visual-Semantic Graph
VSGraph-LC starts from anchor selection referring to the semantic similarity between metadata and correct label concepts, and then propagates correct labels from anchors on a visual graph using graph neural network (GNN).
Ranked #9 on
Image Classification
on WebVision-1000
Webly Supervised Image Classification with Self-Contained Confidence
Therefore, a simple yet effective WSL framework is proposed.
Ranked #7 on
Image Classification
on WebVision-1000
Scale-Equalizing Pyramid Convolution for Object Detection
Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution.
Ranked #88 on
Object Detection
on COCO test-dev
How Does BN Increase Collapsed Neural Network Filters?
This phenomenon is caused by the normalization effect of BN, which induces a non-trainable region in the parameter space and reduces the network capacity as a result.
Gradual Network for Single Image De-raining
Taking the residual result (the coarse de-rained result) between the rainy image sample (i. e. the input data) and the output of coarse stage (i. e. the learnt rain mask) as input, the fine stage continues to de-rain by removing the fine-grained rain streaks (e. g. light rain streaks and water mist) to get a rain-free and well-reconstructed output image via a unified contextual merging sub-network with dense blocks and a merging block.
Learning Efficient Detector with Semi-supervised Adaptive Distillation
ADL enlarges the distillation loss for hard-to-learn and hard-to-mimic samples and reduces distillation loss for the dominant easy samples, enabling distillation to work on the single-stage detector first time, even if the student and the teacher are identical.
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos
Secondly, TSD significantly reduces the computations to run video action recognition with compressed frames on the cloud, while maintaining high recognition accuracies.
Fast Video Shot Transition Localization with Deep Structured Models
In order to train a high-performance shot transition detector, we contribute a new database ClipShots, which contains 128636 cut transitions and 38120 gradual transitions from 4039 online videos.
Ranked #3 on
Camera shot boundary detection
on ClipShots
(using extra training data)
