Search Results for author:
Found 23 papers, 11 papers with code
SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied.
OccFiner: Offboard Occupancy Refinement with Hybrid Propagation
Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision.
PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation
In this paper, we introduce PixArt-\Sigma, a Diffusion Transformer model~(DiT) capable of directly generating images at 4K resolution.
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout.
PixArt-$α$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
We hope PIXART-$\alpha$ will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
Identity-Seeking Self-Supervised Representation Learning for Generalizable Person Re-identification
To overcome the barriers of data and annotation, we propose to utilize large-scale unsupervised data for training.
Generalizable Person Re-identification
Representation Learning
MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation
These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation
These queries are then processed iteratively by a BEV-Evolving decoder, which selectively aggregates deep features from either LiDAR, cameras, or both modalities.
Generalizable Re-Identification from Videos with Cycle Association
In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos.
Domain Generalization
Generalizable Person Re-identification
+1
Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
(3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable.
Self-Supervised Learning via Maximum Entropy Coding
To cope with this issue, we propose Maximum Entropy Coding (MEC), a more principled objective that explicitly optimizes on the structure of the representation, so that the learned representation is less biased and thus generalizes better to unseen downstream tasks.
Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking
In this paper, we design experiments to verify such misfit between global re-ID feature distances and local matching in tracking, and propose a simple yet effective approach to adapt affinity estimations to corresponding matching scopes in MTMCT.
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data.
Do Different Tracking Tasks Require Different Appearance Models?
We show how most tracking tasks can be solved within this framework, and that the same appearance model can be successfully used to obtain results that are competitive against specialised methods for most of the tasks considered.
Ranked #2 on
Video Object Segmentation
on DAVIS 2017
(mIoU metric)
Multi-Object Tracking
Multi-Object Tracking and Segmentation
+10
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
We show that compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.
CycAs: Self-supervised Cycle Association for Learning Re-identifiable Descriptions
This paper proposes a self-supervised learning method for the person re-identification (re-ID) problem, where existing unsupervised methods usually rely on pseudo labels, such as those from video tracklets or clustering.
Circle Loss: A Unified Perspective of Pair Similarity Optimization
This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity $s_p$ and minimize the between-class similarity $s_n$.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Locality Aware Appearance Metric for Multi-Target Multi-Camera Tracking
Due to the continuity of target trajectories, tracking systems usually restrict their data association within a local neighborhood.
Towards Real-Time Multi-Object Tracking
In this paper, we propose an MOT system that allows target detection and appearance embedding to be learned in a shared model.
Ranked #4 on
Multi-Object Tracking
on HiEve
Softmax Dissection: Towards Understanding Intra- and Inter-class Objective for Embedding Learning
The softmax loss and its variants are widely used as objectives for embedding learning, especially in applications like face recognition.
Linkage Based Face Clustering via Graph Convolution Network
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
Query Adaptive Late Fusion for Image Retrieval
That is to say, for some queries, a feature may be neither discriminative nor complementary to existing ones, while for other queries, the feature suffices.
Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed.
