7 accepted papers
Achieving robust generalization from limited data is a central challenge in embodied intelligence. Prevailing methods fail by regressing absolute coordinates, which violates the principle of general covariance. Theoretically, this conflates the intrinsic task geometry with rigid execution patterns, …
Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, repeatedly deciding which positions to commit at each step. Standard decoding follows a greedy rule, unmasking the most confident positions, yet this local choice can lock the model into a suboptimal unmasking…
Distinguishing key features in complex visual tasks is challenging. A novel approach treats image patches (tokens) as waves. By using both phase and amplitude, it captures richer semantics and specific invariances compared to pixel-based methods, and allows for feature fusion across regions for a ho
Regarded as a template-matching task for a long time, visual object tracking has witnessed significant progress in space-wise exploration. However, since tracking is performed on videos with substantial time-wise information, it is important to simultaneously mine the temporal contexts which have no…
Tensor decomposition and reconstruction attention is a promising global context learning approach because it can remain efficient while avoiding feature compression. To exploit its potential even further in visual tracking, we redesign a 3D tensor modeling paradigm, namely tensor Decomposition, Inte…
Discriminative correlation filter (DCF) is a highly efficient tracking technique using the circulant shifted samples of search images to update the template, so the reliability of input samples determines template quality. In this paper, we rethink the reliability problem of input samples in advance…
We explore an innovative view on distribution modulation to boost Siamese trackers. Specially, we observed two cases of possible distribution inconsistency in Siamese tracking: 1) Two branches with different sizes may be in different distribution ranges after a shared backbone (including BN layers).…