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Found 6 papers, 3 papers with code
See What You Seek: Semantic Contextual Integration for Cloth-Changing Person Re-Identification
In this paper, we propose a novel prompt learning framework, Semantic Contextual Integration (SCI), for CC-ReID, which leverages the visual-text representation capabilities of CLIP to minimize the impact of clothing changes and enhance ID-relevant features.
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
We anticipate that the challenges posed by OccludeNet will stimulate further exploration of causal relations in occlusion scenarios and encourage a reevaluation of class correlations, ultimately promoting sustainable performance improvements.
Diversity-Driven Synthesis: Enhancing Dataset Distillation through Directed Weight Adjustment
Specifically, we introduce a novel method that employs dynamic and directed weight adjustment techniques to modulate the synthesis process, thereby maximizing the representativeness and diversity of each synthetic instance.
Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks
In this paper, we propose Hybrid Step-wise Distillation (HSD) method, tailored for neuromorphic datasets, to mitigate the notable decline in performance at lower time steps.
Dynamic Association Learning of Self-Attention and Convolution in Image Restoration
Thus, we propose to refine background textures with the predicted degradation prior in an association learning manner.
Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach
As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks.
Ranked #18 on
Few-Shot Image Classification
on FC100 5-way (5-shot)
