6 papers · 1 filter
iFAN: Inference-Aware Learning for Plain Mask Transformers
Fang Li, Yu He, Haoyang Tong +7
Query-based mask transformers assemble segmentation outputs through pixel-wise competition among query predictions of the final layer, yet this inference process is not explicitly…
Match One, Learn with Graph: One-to-Graph Query Collaboration with Backward Sharing for Object Detection
Wenxiao Fan, Jingling Fu, Luohang Liu +8
One-to-one (O2O) matching enables Detection Transformers (DETRs) to perform end-to-end set prediction by assigning each object to a single positive query. However, the strongest cl…
Recompute or Reuse? Diagnosing and Mitigating Textual Shortcuts in VLM Self-Reflection
Wenxiao Fan, Jingling Fu, Fang Li +9
Vision-language models (VLMs) are expected to revise their reasoning when visual evidence changes. Failures to do so are often attributed to insufficient visual attention or contex…
Leveraging Dissimilarity Invariance as a Robust Anchor for Learning with Noisy Labels
Wenxiao Fan, Kan Li
Deep learning models excel in visual recognition but suffer severe performance drops when training labels are corrupted by noise. Under label noise prior work cannot learn accurate…
Stitch and Tell: A Structured Multimodal Data Augmentation Method for Spatial Understanding
Hang Yin, Xiaomin He, PeiWen Yuan +5
Existing vision-language models often suffer from spatial hallucinations, i.e., generating incorrect descriptions about the relative positions of objects in an image. We argue that…
Combating Semantic Contamination in Learning with Label Noise
Wenxiao Fan, Kan Li
Noisy labels can negatively impact the performance of deep neural networks. One common solution is label refurbishment, which involves reconstructing noisy labels through predictio…