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Found 14 papers, 5 papers with code
Unsupervised Domain Adaptation for Semantic Segmentation with Pseudo Label Self-Refinement
Recent state-of-the-art (SOTA) UDA methods employ a teacher-student self-training approach, where a teacher model is used to generate pseudo-labels for the new data which in turn guide the training process of the student model.
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
In this regard, source-free domain adaptation (SFDA) excels as access to source data is no longer required during adaptation.
Ranked #5 on
Source-Free Domain Adaptation
on VisDA-2017
Cross-View Visual Geo-Localization for Outdoor Augmented Reality
Precise estimation of global orientation and location is critical to ensure a compelling outdoor Augmented Reality (AR) experience.
GraphMapper: Efficient Visual Navigation by Scene Graph Generation
Understanding the geometric relationships between objects in a scene is a core capability in enabling both humans and autonomous agents to navigate in new environments.
SASRA: Semantically-aware Spatio-temporal Reasoning Agent for Vision-and-Language Navigation in Continuous Environments
This paper presents a novel approach for the Vision-and-Language Navigation (VLN) task in continuous 3D environments, which requires an autonomous agent to follow natural language instructions in unseen environments.
Recall Loss for Imbalanced Image Classification and Semantic Segmentation
Many works have proposed to weigh the standard cross entropy loss function with pre-computed weights based on class statistics such as the number of samples and class margins.
RGB2LIDAR: Towards Solving Large-Scale Cross-Modal Visual Localization
To enable large-scale evaluation, we introduce a new dataset containing over 550K pairs (covering 143 km^2 area) of RGB and aerial LIDAR depth images.
Text-based Localization of Moments in a Video Corpus
This task poses a unique challenge as the system is required to perform: (i) retrieval of the relevant video where only a segment of the video corresponds with the queried sentence, and (ii) temporal localization of moment in the relevant video based on sentence query.
A Skip Connection Architecture for Localization of Image Manipulations
In order to exploit these traces in localizing the tampered regions, we propose an encoder-decoder based network where we fuse representations from early layers in the encoder (which are richer in low-level spatial cues, like edges) by skip pooling with representations of the last layer of the decoder and use for manipulation detection.
Weakly Supervised Video Moment Retrieval From Text Queries
The weak nature of the supervision is because, during training, we only have access to the video-text pairs rather than the temporal extent of the video to which different text descriptions relate.
Webly Supervised Joint Embedding for Cross-Modal lmage-Text Retrieval
Inspired by the recent success of web-supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation.
Webly Supervised Joint Embedding for Cross-Modal Image-Text Retrieval
Inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn robust image-text joint representation.
Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval
Constructing a joint representation invariant across different modalities (e. g., video, language) is of significant importance in many multimedia applications.
Ranked #37 on
Video Retrieval
on MSR-VTT
Diversity-aware Multi-Video Summarization
Most video summarization approaches have focused on extracting a summary from a single video; we propose an unsupervised framework for summarizing a collection of videos.
