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Found 10 papers, 4 papers with code
Knowledge Condensation and Reasoning for Knowledge-based VQA
We condense the retrieved knowledge passages from two perspectives.
Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks
Unlike the existing self-supervised learning methods, prior knowledge from human images is utilized in SOLIDER to build pseudo semantic labels and import more semantic information into the learned representation.
Ranked #1 on
Person Search
on PRW
InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation
When using such a pair to predict an object instance on the current frame, not only the generated instance is automatically associated with its precursors on previous frames, but the model gets a good prior for predicting the same object.
Learning Disentangled Label Representations for Multi-label Classification
The specificity of the framework lies in a feature disentangle module, which contains learnable semantic queries and a Semantic Spatial Cross-Attention (SSCA) module.
QueryProp: Object Query Propagation for High-Performance Video Object Detection
The proposed QueryProp contains two propagation strategies: 1) query propagation is performed from sparse key frames to dense non-key frames to reduce the redundant computation on non-key frames; 2) query propagation is performed from previous key frames to the current key frame to improve feature representation by temporal context modeling.
PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentation
To overcome these limitations, we propose a unified framework for the DPS task by applying a dynamic convolution technique to both the PS and depth prediction tasks.
Split Semantic Detection in Sandplay Images
Sandplay image, as an important psychoanalysis carrier, is a visual scene constructed by the client selecting and placing sand objects (e. g., sand, river, human figures, animals, vegetation, buildings, etc.).
Spatial and Semantic Consistency Regularizations for Pedestrian Attribute Recognition
To fully exploit inter-image relations and aggregate human prior in the model learning process, we construct a Spatial and Semantic Consistency (SSC) framework that consists of two complementary regularizations to achieve spatial and semantic consistency for each attribute.
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting
Second, based on the proposed definition, we expose the limitations of the existing datasets, which violate the academic norm and are inconsistent with the essential requirement of practical industry application.
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
Despite various methods are proposed to make progress in pedestrian attribute recognition, a crucial problem on existing datasets is often neglected, namely, a large number of identical pedestrian identities in train and test set, which is not consistent with practical application.
Ranked #3 on
Pedestrian Attribute Recognition
on PETA
