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Omni-Scale Feature Learning for Person Re-Identification
TLDR
A novel deep ReID CNN is designed, termed Omni-Scale Network (OSNet), for omni-scale feature learning by designing a residual block composed of multiple convolutional feature streams, each detecting features at a certain scale.
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
TLDR
This paper forms video summarization as a sequential decision-making process and develops a deep summarization network (DSN) to summarize videos, which is comparable to or even superior than most of published supervised approaches.
Domain Generalization with MixStyle
TLDR
A novel approach based on probabilistically mixing instancelevel feature statistics of training samples across source domains, motivated by the observation that visual domain is closely related to image style, which results in novel domains being synthesized implicitly and hence the generalizability of the trained model.
Deep Domain-Adversarial Image Generation for Domain Generalisation
TLDR
This paper proposes a novel DG approach based on Deep Domain-Adversarial Image Generation based on augmenting the source training data with the generated unseen domain data to make the label classifier more robust to unknown domain changes.
Learning to Generate Novel Domains for Domain Generalization
TLDR
This paper employs a data generator to synthesize data from pseudo-novel domains to augment the source domains, and outperforms current state-of-the-art DG methods on four benchmark datasets.
Learning to Prompt for Vision-Language Models
TLDR
Context Optimization (CoOp) is proposed, a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition that requires as few as one or two shots to beat hand-crafted prompts with a decent margin and is able to gain significant improvements when using more shots.
Learning Generalisable Omni-Scale Representations for Person Re-Identification
TLDR
Novel CNN architectures to address person re-identification and cross-dataset discrepancies are developed, including a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omNI-scale features.
Domain Adaptive Ensemble Learning
TLDR
Extensive experiments show that DAEL improves the state-of-the-art on both problems, often by significant margins.
Domain Generalization: A Survey
TLDR
For the first time, a comprehensive literature review in DG is provided to summarize the developments over the past decade and conduct a thorough review into existing methods and theories.
Generalized Out-of-Distribution Detection: A Survey
TLDR
This survey presents a generic framework called generalized OOD detection, which encompasses the five aforementioned problems, i.e., AD, ND, OSR, OOD Detection, and OD, and conducts a thorough review of each of the five areas by summarizing their recent technical developments.
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