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Found 9 papers, 7 papers with code
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlation attention, (e. g., query-query and key-key attention).
Domain Adaptive Detection of MAVs: A Benchmark and Noise Suppression Network
A new benchmark for cross-domain MAV detection is proposed based on the proposed dataset.
Harmonious Teacher for Cross-Domain Object Detection
Self-training approaches recently achieved promising results in cross-domain object detection, where people iteratively generate pseudo labels for unlabeled target domain samples with a model, and select high-confidence samples to refine the model.
Motion and Appearance Adaptation for Cross-Domain Motion Transfer
Motion transfer aims to transfer the motion of a driving video to a source image.
Revisiting AP Loss for Dense Object Detection: Adaptive Ranking Pair Selection
However, a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed. In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pairs between positive and negative samples. Based on this observation, we propose two strategies to improve the AP loss.
Cross-domain Detection Transformer based on Spatial-aware and Semantic-aware Token Alignment
In particular, we take advantage of the characteristics of cross-attention as used in detection transformer and propose the spatial-aware token alignment (SpaTA) and the semantic-aware token alignment (SemTA) strategies to guide the token alignment across domains.
Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation
Existing works typically treat cross-domain semantic segmentation (CDSS) as a data distribution mismatch problem and focus on aligning the marginal distribution or conditional distribution.
BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation
By integrating the boundary adaptation and prototype alignment, we are able to train a discriminative and domain-invariant model for cross-domain semantic segmentation.
Unbiased Mean Teacher for Cross-domain Object Detection
We reveal that there often exists a considerable model bias for the simple mean teacher (MT) model in cross-domain scenarios, and eliminate the model bias with several simple yet highly effective strategies.
