diff --git a/docs/changelog.md b/docs/changelog.md
index d9ed84a8d1..7445e93b34 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -11,6 +11,7 @@
 
 
 **Bug and Typo Fixes**
+- Fix typo in default argument of BaseHead. ([#446](https://github.com/open-mmlab/mmaction2/pull/446))
 
 **ModelZoo**
 
diff --git a/mmaction/models/heads/base.py b/mmaction/models/heads/base.py
index 92e9b0427f..91abacd124 100644
--- a/mmaction/models/heads/base.py
+++ b/mmaction/models/heads/base.py
@@ -36,7 +36,7 @@ class BaseHead(nn.Module, metaclass=ABCMeta):
         num_classes (int): Number of classes to be classified.
         in_channels (int): Number of channels in input feature.
         loss_cls (dict): Config for building loss.
-            Default: dict(type='CrossEntropyLoss').
+            Default: dict(type='CrossEntropyLoss', loss_weight=1.0).
         multi_class (bool): Determines whether it is a multi-class
             recognition task. Default: False.
         label_smooth_eps (float): Epsilon used in label smooth.
@@ -46,7 +46,7 @@ class BaseHead(nn.Module, metaclass=ABCMeta):
     def __init__(self,
                  num_classes,
                  in_channels,
-                 loss_cls=dict(type='CrossEntropyLoss', loss_factor=1.0),
+                 loss_cls=dict(type='CrossEntropyLoss', loss_weight=1.0),
                  multi_class=False,
                  label_smooth_eps=0.0):
         super().__init__()
diff --git a/tests/test_models/test_head.py b/tests/test_models/test_head.py
index b05d85f05b..272fdeadaf 100644
--- a/tests/test_models/test_head.py
+++ b/tests/test_models/test_head.py
@@ -6,7 +6,7 @@
 
 
 class ExampleHead(BaseHead):
-    # use a ExampleHead to success BaseHead
+    # use an ExampleHead to test BaseHead
     def init_weights(self):
         pass
 
@@ -24,6 +24,15 @@ def test_base_head():
     assert 'loss_cls' in losses.keys()
     assert losses.get('loss_cls') > 0, 'cls loss should be non-zero'
 
+    head = ExampleHead(3, 400, dict(type='CrossEntropyLoss', loss_weight=2.0))
+
+    cls_scores = torch.rand((3, 4))
+    # When truth is non-empty then cls loss should be nonzero for random inputs
+    gt_labels = torch.LongTensor([2] * 3).squeeze()
+    losses = head.loss(cls_scores, gt_labels)
+    assert 'loss_cls' in losses.keys()
+    assert losses.get('loss_cls') > 0, 'cls loss should be non-zero'
+
 
 def test_i3d_head():
     """Test loss method, layer construction, attributes and forward function in