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Found 9 papers, 6 papers with code
Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking
Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations.
History-Aware Conversational Dense Retrieval
To address the aforementioned issues, we propose a History-Aware Conversational Dense Retrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.
Attack and Defense Analysis of Learned Image Compression
Our experiments compare the effects of different dimensions such as attack methods, models, qualities, and targets, concluding that in the worst case, there is a 61. 55% decrease in PSNR or a 19. 15 times increase in bpp under the PGD attack.
Collaboration and Transition: Distilling Item Transitions into Multi-Query Self-Attention for Sequential Recommendation
Second, we develop a transition-aware embedding distillation module that distills global item-to-item transition patterns into item embeddings, which enables the model to memorize and leverage transitional signals and serves as a calibrator for collaborative signals.
Knowledge Combination to Learn Rotated Detection Without Rotated Annotation
Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used instead.
Learning Instance and Task-Aware Dynamic Kernels for Few Shot Learning
A principle way of achieving few-shot learning is to realize a model that can rapidly adapt to the context of a given task.
Learning Online for Unified Segmentation and Tracking Models
A segmentation model cannot easily learn from prior information given in the visual tracking scenario.
Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
Tracking a time-varying indefinite number of objects in a video sequence over time remains a challenge despite recent advances in the field.
Learn to Predict Sets Using Feed-Forward Neural Networks
In our formulation we define a likelihood for a set distribution represented by a) two discrete distributions defining the set cardinally and permutation variables, and b) a joint distribution over set elements with a fixed cardinality.
