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Found 12 papers, 4 papers with code
M2-RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval
We then summarize this empirical study into the M2-RAAP recipe, where our technical contributions lie in 1) the data filtering and text re-writing pipeline resulting in 1M high-quality bilingual video-text pairs, 2) the replacement of video inputs with key-frames to accelerate pre-training, and 3) the Auxiliary-Caption-Guided (ACG) strategy to enhance video features.
EVE: Efficient zero-shot text-based Video Editing with Depth Map Guidance and Temporal Consistency Constraints
Compared with images, we conjecture that videos necessitate more constraints to preserve the temporal consistency during editing.
Generalized Relation Modeling for Transformer Tracking
Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain.
SOAR: Scene-debiasing Open-set Action Recognition
Deep models have the risk of utilizing spurious clues to make predictions, e. g., recognizing actions via classifying the background scene.
AiATrack: Attention in Attention for Transformer Visual Tracking
However, the independent correlation computation in the attention mechanism could result in noisy and ambiguous attention weights, which inhibits further performance improvement.
Ranked #2 on
Visual Object Tracking
on NeedForSpeed
Distilling Inter-Class Distance for Semantic Segmentation
Knowledge distillation is widely adopted in semantic segmentation to reduce the computation cost. The previous knowledge distillation methods for semantic segmentation focus on pixel-wise feature alignment and intra-class feature variation distillation, neglecting to transfer the knowledge of the inter-class distance in the feature space, which is important for semantic segmentation.
Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking
In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion.
Multi-Person Pose Estimation
Multi-Person Pose Estimation and Tracking
+1
Discriminative Feature Transformation for Occluded Pedestrian Detection
Such a feature transformation partially compen- sates the missing contribution of occluded parts in feature space, therefore improving the performance for occluded pedestrian detection.
Bi-box Regression for Pedestrian Detection and Occlusion Estimation
The full body estimation branch is trained to regress full body regions for positive pedestrian proposals, while the visible part estimation branch is trained to regress visible part regions for both positive and negative pedestrian proposals.
Actor-Action Semantic Segmentation with Region Masks
One major challenge for this task is that when an actor performs an action, different body parts of the actor provide different types of cues for the action category and may receive inconsistent action labeling when they are labeled independently.
Attention to Head Locations for Crowd Counting
Occlusions, complex backgrounds, scale variations and non-uniform distributions present great challenges for crowd counting in practical applications.
Multi-Label Learning of Part Detectors for Heavily Occluded Pedestrian Detection
Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns.
